精选嫩鲍 Applied and Computational Math Colloquium
Upcoming Talks:精选嫩鲍
精选嫩鲍
期间: 9月19日上昼10-11点
所在：双清C654
酬金东说念主：王旭
使命单元：中国科学院数学与系统科学辩论院
酬金题目：Stochastic inverse problems for both time-harmonic and time-dependent wave equations
The stochastic inverse problem originates from practical issues such as resource exploration， medical imaging， and stealth technology， using stochastic differential equations as mathematical models to describe the problem under the interference of random factors such as environmental uncertainties and data noise. This talk will introduce the uniqueness， stability， and computational methods of inverse problems for random sources and random potentials in the context of both time-harmonic and time-dependent wave equations.
王旭，中国科学院数学与系统科学辩论院副辩论员。2018年博士毕业于中科院数学与系统科学辩论院，2018-2021年任好意思国普渡大学Golomb打听助理锤真金不怕火，2021年入职中国科学院数学与系统科学辩论院。赢得中国科学院引才筹画后生方法、国度当然科学基金委优秀后生科学基金方法（外洋）等资助，主要从事立时波动方程反问题、立时偏微分方程数值方法等辩论。
Past Talks:
Title: Adaptive Gradient Methods with Energy for Optimization Problems
Speaker: 刘海亮 (Hailiang Liu)， Iowa State University
Time: 15:20-16:20 on Tuesday， July 4th， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building
Abstract:
We propose AEGD， a new algorithm for gradient-based optimization of stochastic objective functions， based on adaptive updates of quadratic energy. The method is shown to be unconditionally energy stable， irrespective of the step size. In addition， AEGD enjoys tight convergence rates， yet allows a large step size. The method is straightforward to implement and requires little tuning of hyper-parameters. Experimental results demonstrate that AEGD works well for various optimization problems: it is robust with respect to initial data， capable of making rapid initial progress， shows comparable and most times better generalization performance than SGD with momentum for deep neural networks.
Bio：
Hailiang Liu is a Professor of Mathematics and Computer Science at the Iowa State University (ISU). He earned his Bachelor degree from Henan Normal University， Master degree from Tsinghua University， and Ph.D. degree from the Chinese Academy of Sciences， all in Mathematics. His research interests include analysis of partial differential equations， the development of high order numerical algorithms for solving these PDE problems， with diverse applications. He is the author of over 160 peer reviewed papers， and the recipient of many
awards and honors， including the Alexander von Humboldt-Research Fellow， and the inaugural Holl Chair in Applied Mathematics at Iowa State University.
Email: hliu@iastate.edu.cn
Title: Multiscale Modeling of Arctic Sea Ice Floes
Title: Generalized Lax equivalence theory for the compressible Navier-Stokes system
Speaker: 佘邦伟，齐门师范大学
Time: 16:00-17:00 on Thursday， June 8th， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building
Abstract:
In this talk we discuss a generalized Lax equivalence theory for the compressible Navier-Stokes system: convergence = stability + consistency. First， we introduce the concept of consistent approximation representing the (energy/entropy) stability and consistency of suitable numerical solutions. Then， by passing to the limit we obtain a dissipative weak solution with the only information derived from the stability of the consistent approximation. Further， we show that the dissipative weak solution coincide with the strong solution as long as the latter exists. Consequently， any numerical solution belonging to the class of consistent approximation convergences to the strong solution. Finally， we present a numerical example.
Bio：
佘邦伟，毕业德国好意思茵茨大学，曾任捷克科学院数学所准聘、长聘辩论员，现为齐门师范大学辩论员，主要辩论标的为流膂力学方程组的数值分析和模拟，在Numer. Math.、IMA J Numer. Anal.、SIAM J Numer. Anal.、Math. Comput.、J Comput. Phys.、Multi. Model Simul.等忖度数学限制的知名期刊发表多篇学术论文，著有Springer专著一部。
Title: 神经汇集架构表面与应用的结合
Speaker: Chen Wuyang
Time: Wed.， 10:00-11:00 am， June 7， 2023
Online Tencent: 720-234-077
Abstract:
本酬金将通过表面和本质分析深度神经汇集的架构对其敛迹速率、复杂度、泛化性能的影响，并进一步在莫得任何考试本钱的情况下，对汇集架构的假想进行准确和高效的迷惑。将来，咱们将进一步欺骗深度学习表面对考试神经汇集的数据和优化历程进行迷惑和展望。本酬金也将浅近先容Simon Fraser University和加拿大温哥华。
Bio:
陈武阳博士 (https://chenwydj.github.io/) 将于 2024 年秋季加入Simon Fraser University忖度机系担任助理锤真金不怕火。2023 - 2024年，陈武阳博士将加入UC Berkeley统计系当作博士后。陈武阳本科毕业于中国科学时期大学，并于2023年赢得好意思国德州大学奥斯汀分校电子忖度机工程系的博士学位。陈武阳博士的使命发表于各大顶会期刊 (NeurIPS， ICML， ICLR， JMLR， CVPR， ICCV， ECCV)， 他的辩论竣事在2022年被好意思国国度科学基金会 (National Science Foundation， NSF) 的网站新闻报说念: https://www.nsf.gov/news/news_summ.jsp?cntn_id=304639&org=CISE
Title: Responsible Machine Learning and Machine Learning for Science
Speaker: Chen Tianlong
Time: Wed.， 9:00-10:00 am， June 7， 2023
Online Tencent: 720-234-077
Abstract:
本酬金分三部分：(一) 怎样开采高效和可靠的机器学习系统；(二) 怎样假想机器学习算法来经管科常识题，比如：药物开采和量子忖度；(三) 对北卡罗来纳大学教堂山分校的浅近共享。
Bio:
陈天龙博士 (https://tianlong-chen.github.io/) 将于 2024 年秋季加入北卡罗来纳大学教堂山分校忖度机系担任助理锤真金不怕火。在这之前 (2023 - 2024)，他会加入麻省理工和哈佛大学担任博士后辩论员。陈天龙博士划分于 2023 年和 2017 年赢得了德州大学奥斯汀分校电子&忖度机工程的博士学位，和中国科学时期大学 (少年班学院) 的应用数学和忖度机双学士学位。他的主要辩论标的是开采准确、可靠和高效的机器学习系统。他近期的辩论标的主要聚焦于：(1) 迫切的机器学习问题，包括稀少神经汇集、鲁棒性、可学习的优化算法、图汇集和扩散模子；(2) 交叉学科的科膏火事，比如生物工程、疫苗药物研发和量子忖度。陈天龙博士曾先后赢得了 Adobe 博士奖学金、IBM 博士奖学金、UT Austin 辩论生院长奖学金和 LoG’22 最好论文。陈天龙博士曾于各大顶会顶刊发表论文，包括 NeurIPS， ICML， ICLR， JMLR， CVPR， ICCV， ECCV， AAAI， TAPMI 等。
Title: From kinetic flocking model of Cucker-Smale type to self-organized hydrodynamic model
Speaker: 张起飞，中国地质大学（武汉）
Time: 16:00-17:00 on Thursday， June 1st， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building
Abstract:
In this talk， I will discuss our recent results on the hydrodynamic limit problem for a kinetic flocking model of Cucker-Smale type. Using the Cucker-Smale model as an example， we develop systematically a GCI-based expansion method， and micro-macro decomposition on the dual space， to justify the limits to the macroscopic system， a non-Euler type hyperbolic system. We believe our method has widely application in the collective motions and active particle systems. This is a joint work with Prof. Ning JIANG and Prof. Yi-Long LUO.
Bio：
张起飞，中国地质大学（武汉）数学与物理学院副锤真金不怕火。主要辩论限制为偏微分方程，包括复杂流体、宏微不雅耦合模子、分子动理学表面等方面，主要竣事发表在ARMA、SIAM-JMA、CVPDE、JDE等国际学术期刊。
Title: Resource Constrained Revenue Management with Demand Learning and Large Action Spaces
Speaker: Yining Wang， University of Texas at Dallas
Time: 16:10-17:10 on Friday， May 26， 2023
Venue: W11， Ning Zhai
Abstract:
In this talk I will present my recent works on resource constrained revenue management with demand learning and large action spaces. We study a class of well-known RM problems such as dynamic pricing and assortment optimization subject to non-replenishable inventory constraints， where demand or choice model information is unknown a priori and needs to be estimated， and the action spaces (price vectors， assortments) are large. We present a general primal-dual optimization algorithm with upper confidence bounds to achieve optimal asymptotic regret. We also extend this result to nonparametric demand modeling in network revenue management problems via a robust ellipsoid method.
Paper links:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3841273
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3948140
Bio：
Yining Wang is an associate professor of operations management at Naveen Jindal School of Management， University of Texas at Dallas. He graduated with a PhD in Machine Learning from Carnegie Mellon University. His research primarily focuses on machine learning and online learning methodology with applications in operations and revenue management. He is also interested in ethics questions arising from the use of machine learning and AI in personalized revenue management systems， such as data privacy protection and decision fairness issues.
Title: Self-supervised Deep Learning for Solving Inverse Problems in Imaging
Speaker：Ji Hui (纪辉)，National University of Singapore
Time：16:00-17:00， May 24， 2023
Venue: Online Tencent Meeting ID：568-971-945
Abstract:
Deep learning has proved to be a powerful tool in many domains， including inverse imaging problems. However， most existing successful deep learning solutions to these inverse problems are based on supervised learning， which requires many ground-truth images for training a deep neural network (DNN). This prerequisite on training datasets limits their applicability in data-limited domains， such as medicine and science. In this talk， we will introduce a series of works on self-supervised learning for solving inverse imaging problems. Our approach teaches a DNN to predict images from their noisy and partial measurements without seeing any related truth image， which is achieved by neuralization of Bayesian inference with DNN-based over-parametrization of images. Surprisingly， our proposed self-supervised method can compete well against supervised learning methods in many real-world imaging tasks.
Bio:
Dr. Ji Hui obtained his Ph.D. in Computer Science from the University of Maryland at College Park in 2006. He currently is an Associate Professor at the Department of Mathematics and serves as the Deputy Director of the Centre for Data Science and Machine Learning at NUS. He serves on the editorial boards of several research journals， including the SIAM Journal on Imaging Sciences. His research interests lie in computational harmonic analysis， computational vision， imaging science， and machine learning.
Title: Development of physics-based and data-based molecular simulation methods
哔哩哔哩官网在线观看Speaker: Gao Yiqin， Peking University
Time: Tues.， 10:00-11:00 am， May 23， 2023
Venue: Conference Room 1， Jin Chun Yuan West Bldg. (近春园西楼第一会议室)
Organizer: Bao ChenglongAbstract:
Recently， molecular simulations have benefited greatly from the development and subsequent application of deep-learning methods. In this talk， we will discuss how machine-learning methods can be combined with enhanced sampling techniques to speed up molecular dynamic simulations. We will then discuss about our recent effort on learning from Alphafold2 to reproduce and improve protein structure prediction AI models. All these methods are implemented in our home-made molecular simulation package， SPONGE， an MD software rewritten using MindSpore and highly compatible with the deep-learning platform. Through these efforts， we try to generate a multi-functional package for structure prediction， molecule and sequence generation， structure evaluation， and dynamics simulation. We will also discuss the possible applications of these methods in physical， chemical and biological problems.
酬金东说念主：高毅勤
1972 年降生，1993 年本科毕业于四川大学化学系，1996 年在中科院化学所赢得硕士学位，2001 年赢得加州理工学院博士学位。2001 年－ 2004 年在加州理工学院和哈佛大学作念博士后辩论。2004 年 -2010 年在好意思国德克萨斯农工大学（Texas A&M University）化学系任助理锤真金不怕火；2010 年起任北京大学化学与分子工程学院锤真金不怕火，2013 年起同期担任北京大学生物医学前沿编削中心辩论员。主要从事生物物理化学/ 表面化学方面的基础辩论。现任北京大学理学部副主任，JCTC杂志副主编 。
Title: Evolving finite element approximations with artificial tangential motion for surface evolution under a prescribed velocity field
Speaker: 李步杨，香港理工大学应用数学系
Time: 16:00-17:00 on Monday， May 15， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building
Abstract:
A novel evolving surface finite element method is proposed to compute the evolution of a closed hypersurface $\Gamma\subset\R^d$， $d=2，3$， moving under a prescribed smooth velocity field $u$. An modified velocity $v$ with an artificial tangential motion is proposed to minimize the instantaneous rate of deformation of the evolving surface， i.e.， to minimize the energy functional $\int_{\Gamma}|\nabla_\Gamma v|^2$ under the pointwise constraint $v\cdot n = u\cdot n$， in order to improve the mesh quality of the approximate evolving surface. In order to establish a complete stability and convergence theory for the parametric finite element approximations with artificial tangential motion， we reformulate the problem equivalently in terms of the transport equations of the normal vector $n$ and the second fundamental form $\nabla_\Gamma n$. A novel weak formulation and parametric finite element method are proposed for the reformulated system which couples the modified velocity equation (with artificial tangential motion) with the transport equations of $n$ and $\nabla_\Gamma n$. Optimal-order convergence of the semi-discrete parametric finite element method is proved for finite elements of polynomial degree $k\geq 3$. Numerical examples are presented to illustrate the convergence of the proposed method and the performance of the method in improving the mesh quality of the approximate surface.
Bio：
李步扬博士 2012 年于香港城市大学赢得博士学位，先后在南京大学、（德国）图宾根大学、香港理工大学从事科研和素养，现为香港理工大学应用数学系副锤真金不怕火，忖度数学杂志 SIAM Journal on Numerical Analysis， Mathematics of Computation， IMA Journal of Numerical Analysis等杂志编委。主要辩论限制为偏微分方程的数值忖度和数值分析，包括曲率流的有限元靠拢和分析、非线性色散和波动方程不光滑解的忖度方法、不行压 Navier–Stokes 方程的忖度和分析、高频 Helmholtz 方程的有限元和 PML 方法、非线性抛物方程、相场方程、分数阶偏微分方程、Ginzburg-Landau 超导体方程、热敏电阻方程的数值分析，以及有限元法、谱方法、convolution quadrature，等等。
Title: Quantum speedup of Monte Carlo methods and Markov Chains
Speaker: Jin-Peng Liu， the Center for Theoretical Physics， MIT
Time: 15:00-16:00 on Thursday， May 11， 2023
Venue: Online meeting， Tencent 394-2709-5975
Abstract:
Sampling from a given distribution is a fundamental computational problem and has broad applications in statistics， machine learning， physics， etc. We systematically investigate the quantum speedup of Monte Carlo methods， quantum mean estimation， and fast-forwarding of reversible Markov chains. We develop quantum algorithms for sampling log-concave distributions (with density e^{-f(x)} for convex f(x)) and for estimating their normalizing constants， achieving polynomial speedups in query complexity over the best-known classical algorithms. This is a joint work with Andrew M. Childs， Tongyang Li， Chunhao Wang， and Ruizhe Zhang.
Reference:
[1] Quantum algorithms for sampling log-concave distributions and estimating normalizing constants. https://arxiv.org/abs/2210.06539
Bio：
Jin-Peng Liu is a Simons Quantum Postdoctoral Fellow at Simons Institute， UC Berkeley in 2022-2023 (hosted by Umesh Vazirani and Lin Lin). He will be a Postdoctoral Associate at the Center for Theoretical Physics， MIT in 2023-2024 (hosted by Aram Harrow). He received a Ph.D. in applied mathematics at University of Maryland in 2022 spring (advised by Andrew Childs). He received a B.S. in math at Beihang University and Chinese Academy of Sciences Hua Loo Keng Class (supervised by Ya-xiang Yuan and Cong Sun).
Jin-Peng is serving as an editor of the journal Quantum from 2023. He received the NSF QISE-NET Triplet Award in 2021. His research focuses on Quantum for Science. He attempts to develop， analyze， and optimize provably efficient quantum algorithms for computational challenges in natural and data sciences， including quantum simulations， quantum ODE/PDE solvers， q-sampling， and quantum machine learning.
Title: Exploring Ionic Channels and Cell Membranes Dynamics
Speaker: Hamidreza Mofidi， BIMSA
Time: 10:00-11:00 on Wednesday， April 26， 2023
Venue: Conference Room 1， Jin Chun Yuan West Building
Abstract:
In my talk， I first show our study on the electrodiffusion in Ionic Channels via Poisson-Nernst-Planck Models and examine ion size effects on the flow rate of matter through a cross-section. In the second part， I focus on the effects of NMDA receptors on membrane excitation via Morris-Lecar Model. We apply bifurcation analysis and geometric singular perturbation theory to analyze the system. We also study the synchrony analysis in networks of the coupled system.
Bio：
I am a postdoc at the BIMSA Center， supported by the Yau Center at Tsinghua Uni. Before that， I was a VAP at the Uni. of Iowa， USA， working with Math. and Comp. Biology group in the Mathematics Department. My study lies in applied dynamical systems of DEs， math biology， and PDEs. Specifically， I am interested in using techniques such as GSP theory and Bifurcation analysis in nonlinear dynamical systems to model and analyze biological phenomena and study multiple time/spatial scale dynamics.
Title: 新式冷冻电镜原位结构想法时期过甚应用
A new cryo-electron microscopy method to solve the structure of protein complexes in situ and its applications
Speaker: 章新政，中国科学院生物物理辩论所
Time: 16:00-17:00 on Thursday， April 26th， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building.
Abstract:
冷冻电镜电子断层以及亚单元平均方法依然在细胞样品上赢得金标样核糖体优于4埃的结构想法。尽管核糖体品貌极高，其结构想法仍然需要辘集数天数据，且三维分类后的构象平平分辨率在6~7埃。咱们发展了新式原位结构想法方法，不错高通量、高分辨率的赢得原位结构。咱们在冷冻细胞切片数据上赢得超等捕光复合物（3.2埃）以及核糖体（2.9埃）的结构想法，其中核糖体赢得20个4埃傍边的构象。此酬金也将敷陈该方法的基应承趣和适用条款。
Combining the cryo-electron tomography and sub-tomogram averaging， the state of art method is able to directly solve ribosome， a gold standard sample to a resolution beyond 4 Å in cellular environment. Although， the abundancy of ribosome is extremely high in cell， the data collection for a tomographic dataset lasted for couple of days. During to the lack of particles， after 3D classification， an average resolution of 6~7 Å was achieved in different conformations of ribosome. We have developed a new cryo-EM method to solve protein structures in cell with high throughput and high resolution. Using this method， we have determined PBS-PSI-PSII-LHCII supercomplexes and ribosome at 3.2 Å and 2.9 Å， respectively. For ribosome， we calculated 20 conformations with an averaged resolution beyond 4 Å. This talk will be also about the fundamental theory and the application range of the new method.
酬金东说念主简介:
章新政，男，中国科学院生物物理辩论所生物大分子国度重心本质室课题组长、辩论员。2008年赢得北京大学博士学位，师从俞大鹏院士，从事电子显微学相干辩论，2009年赴好意思开展博士后辩论，师从著名结构生物学家Michael Rossmann 院士，开展病毒粒子的冷冻电镜结构生物学辩论。2014 年归国在生物物理所开采孤苦辩论组。主要标的为冷冻电镜新时期、新方法的开采，普及冷冻电镜对纯化卵白质以及细胞内卵白质的三维结构想法智商。连年来在Nature， Science， Cell， Cell Research， Nature Communications等杂志上共发表了通信/共通信论文多篇。
Xinzheng Zhang， Institute of Biophysics， Chinese Academic of Science， Principal Investigator. Xinzheng got his Ph.D degree in Physics on 2008， supervised by Prof. Dapeng Yu. He went to Purdue University， United of States on 2009 as a post-doc to study the 3D structure of viruses with Prof. Michael G Rossmann. Xinzheng returned back to China on 2014 and started to lead an independent research group in Institute of Biophysics. He focused on developing of cryo-EM technologies， including single particle reconstruction method， cryo-EM sample freezing methods and cryo-EM method to solve the protein structure in vivo. In the recent years， Xinzheng published his works on journals such as Nature， Science， Cell， Cell Research and Nature Communications as a corresponding or co-corresponding author.
Title: 地球物理油气勘测中的优化反演问题
Speaker: 李翔精选嫩鲍，中国石油东方地球物理公司
Time: 16:00-17:00 on Thursday， April 13th， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building.
Abstract:
地震勘测是在地表激励震源，通过地下介质的传播，被布设在地表的检波器吸收。通过处理地表吸收的信号，咱们不错得到地下结构，从而判断油气储层的位置和储量。在地震油气勘测限制中，存在多半线性和非线性的优化反演问题。本酬金主要经营了地震油气勘测中的三个主要本色问题。
第一个问题是缺失的地震数据重构。在本色数据的采蚁合，由于各式地表复杂构造的原因，咱们不竭缺失好多震源激励的位置。因此，不错通过构建线性反演问题来重构缺失数据。
第二个问题是井中地震的屡次波成像问题。由于井中地震震源位置不易确信，屡次波成像时期不错得手地幸免对震源信息的需求。
临了，咱们经营了全波形反演时期。该时期通过求解非线性优化问题反演地震波在地下传播介质的性质。本文经营的该方法在陆地复杂地区中的应用。
酬金东说念主简介：
李翔，2007年及2009年于吉林大学地球探伤科学域时期学院划分赢得应用地球物理学士及硕士学位，2015于加拿大英属哥伦比亚大学地震正演成像本质室（Seismic Laboratory for Imaging and Modeling）赢得地球物理博士学位，师从Felix Herrmann锤真金不怕火。毕业之后在PGS公司使命，担任地球物理辩论员。2017年加入中国石油东方地球物理公司，担任休斯顿高档时期各人。
Title: Proximal linearization methods for Schatten p-quasi-norm minimization
Speaker: Chao Zeng， Nankai University
Time:10:00-11:00 on Monday， April 10th， 2023
Venue: Conference Room 1， Floor 1， Jin Chun Yuan West Building
Abstract:
Schatten p-quasi-norm minimization has advantages over nuclear norm minimization in recovering low-rank matrices. However， Schatten p-quasi-norm minimization is much more difficult， especially for generic linear matrix equations. We first extend the lower bound theory of l_p minimization to Schatten p-quasi-norm minimization. Motivated by this property， we propose a proximal linearization method， whose subproblems can be solved efficiently by the (linearized) alternating direction method of multipliers. The convergence analysis of the proposed method involves the nonsmooth analysis of singular value functions. We give a necessary and sufficient condition for a singular value function to be a Kurdyka–Lojasiewicz function. The subdifferentials of related singular value functions are computed. The global convergence of the proposed method is established under some assumptions. Experiments on matrix completion， Sylvester equation and image deblurring show the effectiveness of the algorithm.
酬金东说念主简介：
曾超，南开大学副锤真金不怕火。本科和博士毕业于中科大，之后在南开大学、香港浸会大学、香港大学等学校作念博士后辩论。辩论标的为数值代数、数值优化、图像处理。在Numer. Math.，SIAM J. Numer. Anal.， SIAM J. Matrix Anal. Appl. ， SIAM J. Imaging Sci.等期刊发表论文十余篇。
Title: A class of efficient Hamiltonian conservative spectral methods for Korteweg-de Vries equations
Speaker: Waixiang Cao， Beijing Normal University
Time: 16:00-17:00 on Thursday， April 6th， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building
Abstract:
In this talk， we present and introduce two efficient Hamiltonian conservative fully discrete numerical schemes for Korteweg-de Vries equations. The new numerical schemes are constructed by using time-stepping spectral Petrov-Galerkin (SPG) or Gauss collocation (SGC) methods for the temporal discretization coupled with the $p$-version/spectral local discontinuous Galerkin (LDG) methods for the space discretization. We prove that the fully discrete SPG-LDG scheme preserves both the momentum and the Hamilton energy exactly for generalized KdV equations. While the fully discrete SGC-LDG formulation preserves the momentum and the Hamilton energy exactly for linearized KdV equations. As for nonlinear KdV equations， the SGC-LDG scheme preserves the momentum exactly and is Hamiltonian conserving up to some spectral accuracy. Furthermore， we show that the semi-discrete $p$-version LDG methods converge exponentially with respect to the polynomial degree. The numerical experiments are provided to demonstrate that the proposed numerical methods preserve the momentum， $L^2$ energy and Hamilton energy and maintain the shape of the solution phase efficiently over long time period.
酬金东说念主简介：
曹外香，北京师范大学数学科学学院副锤真金不怕火，辩论标的为偏微分方程数值解法和数值分析，主要辩论有限元方法、有限体积方法，终止有限元方法高效高精度数值忖度。主要竣事发表在SIAM J. Numer. Anal.， Math. Comp.， J. Sci. Comput.， J. Comput. Phys. 等期刊上。曾获中国博士后基金一等资助和尽头资助，广东省当然科学二等奖，主捏国度当然科学基金后生基金一项，面上方法两项。
Title: An arbitrarily high order unfitted finite element method for elliptic interface problems with automatic mesh generation
Speaker: Yong Liu， Academy of Mathematics and Systems Science， Chinese Academy of Sciences.
Time: 10:00-11:00 on Tuesday， March 28， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building.
Abstract:
In this talk， we consider the reliable implementation of high-order unfitted finite element methods on Cartesian meshes with hanging nodes for elliptic interface problems. We construct a reliable algorithm to merge small interface elements with their surrounding elements to automatically generate the finite element mesh whose elements are large with respect to both domains. We propose new basis functions for the interface elements to control the growth of the condition number of the stiffness matrix in terms of the finite element approximation order， the number of elements of the mesh， and the interface deviation which quantifies the mesh resolution of the geometry of the interface. Numerical examples are presented to illustrate the competitive performance of the method.
Bio：
刘勇，中科院数学与系统科学辩论院，忖度数学所，优秀后生副辩论员。划分于2015年，2020年获中国科学时期大学学士和博士学位。2018年至2020年在好意思国布朗大学应用数学系聚拢培养。2020年至2022年在中科院数学与系统科学辩论院，华罗庚数学科学中心作念博士后。主要辩论限制为高精度数值忖度方法，包括终止有限元方法的算法假想过甚数值分析、磁流膂力学方程的数值模拟、非拟合网格有限元方法等。曾获2020年中科院院长奖尽头奖，2021年中科院优博。2023年，获国度当然科学基金后生方法，入选中科院后生编削促进会，入选中科院数学与系统科学辩论院“陈景润往日之星”东说念主才筹画。在SINUM， SISC， JCP等SCI期刊发表论文10余篇。
Title: Conservative， Positivity Preserving and Energy Dissipative Numerical Methods for the Poisson-Nernst-Planck Equations
Speaker: Zhongming Wang， Florida International University
Time: 10:00-11:00 on Thursday， March 23， 2023
Venue: Tencent Meeting: 394-2709-5975
Abstract:
We design and analyze some numerical methods for solving the Poisson-Nernst-Planck (PNP) equations. The numerical schemes， including finite difference method and discontinuous Galerkin method， respect three desired properties that are possessed by analytical solutions: I) conservation， II) positivity of solution， and III) free-energy dissipation. Advantages of different types of methods are discussed. Numerical experiments are performed to validate the numerical analysis. Modified PNP system that incorporating size and solvation effect is also studied to demonstrate the effectiveness of our schemes in solving realistic problems.
This is joint work with D. Jie， H. Liu， P. Yin， H. Yu and S. Zhou.
Bio：
Zhongming Wang， Ph.D.， received his bachelor's degree in Computing Mathematics from City University of Hong Kong in 2003 and doctorate degree in Applied Mathematics from Iowa State University in 2008. He was a Postdoctoral Fellow at the University of California， San Diego from 2008 to 2011. He joined the Department of Mathematics and Statistics， Florida International University in 2011 and was promoted to associate professor in 2016.
Dr. Wang's research interests are in computational and applied mathematics. In particular， he has been working on
1. level set methods for high frequency wave propagations， two-phase flows and the Euler-Poisson equations;
2. direct Discontinuous Galerkin method for nonlinear Fokker-Planck equations
3. conservative， positivity preserving and energy dissipative numerical methods for the Poisson-Nernst-Planck equations.
Title: Quadrature-based moment methods for kinetic equations: Stability analysis and multidimensional models
Speaker: Qian Huang， Tsinghua University
Time: 16:00-17:00 on Thursday， March 16th， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building
Abstract:
Numerical solution of the kinetics equation is crucial to engineering applications， but remains an extremely challenging task. In recent years， the quadrature-based moment methods (QBMM) are shown as effective numerical methods which preserve positivity of the distributions， have the conservation form and are numerically efficient. However， the mathematical theory of QBMM largely incomplete， and it is difficult to extend the existing approaches to tackle multidimensional problems. In this talk， I will present our recent efforts to clarify the well-posedness of QBMM. The hyperbolicity， realizability and dissipative properties of the moment closure systems are investigated. Then， we develop a discrete-velocity-direction model (DVDM) with minimum entropy principle as a novel and convenient multidimensional extension of the QBMM. In the DVDM， the molecular motion is confined to some prescribed directions but the speed is still a continuous variable in each orientation. Numerical tests with a series of 1-D and 2-D flow problems show the efficiency of the DVDM.
简介
黄骞，清华大学能动系助研，于2017年毕业于清华大学获博士学位。主要从事动理学矩方法、清洁低碳毁灭时期等限制辩论，承担当然基金等十余项课题，曾获栽植部当然科学一等奖（2022）。https://www.researchgate.net/profile/Qian-Huang-6
Title: Parametric polynomial preserving recovery on manifolds
Speaker: Hailong Guo， The University of Melbourne， Australia.
Time: 16:00-17:00， Thursday， March 9th， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Building
Abstract:
In this talk， we will introduce gradient recovery schemes for data defined on discretized manifolds. The proposed method， parametric polynomial preserving recovery (PPPR)， does not require the tangent spaces of the exact manifolds which have been assumed for some significant gradient recovery methods in the literature. Another advantage of PPPR is that superconvergence is guaranteed without the symmetric condition which is required in the existing techniques. We also will talk about several applications.
Bio:
郭海龙，2015年博士毕业于好意思国韦恩州立大学，于2015年到2018年在好意思国加州大学圣塔芭芭拉分校任打听助理锤真金不怕火，2018年加入澳大利亚墨尔本大学，先后历任讲师和高档讲师。主要辩论敬爱敬爱包括有限元超敛迹后处理、界面问题的数值方法、机器学习算法等，在Comput. Methods Appl. Mech. Engrg.， J. Comput. Phys.， Math. Models Methods Appl. Sci.， Math. Comp.， Numer. Math.， SIAM J. Numer. Anal.， SIAM J. Sci. Comput. 等主流期刊发表论文20多篇。
Title: Efficient natural gradient method for large-scale optimization problems
Speaker: Yunan Yang， ETH Zürich， Switzerland.
Time: 10:00-11:00 am， Wednesday， March 8th， 2023
Venue: Conference Room 1， Floor 1， Jin Chun Yuan West Building
Abstract:
First-order methods are workhorses for large-scale optimization problems， but they are often agnostic to the structural properties of the problem under consideration and suffer from slow convergence， being trapped in bad local minima， etc. Natural gradient descent is an acceleration technique in optimization that takes advantage of the problem's geometric structure and preconditions the objective function's gradient by a suitable "natural" metric. Despite its success in machine learning， the natural gradient descent method is far from a mainstream computational technique due to the computational complexity of calculating and inverting the preconditioning matrix. This work aims at a unified computational framework and streamlining the computation of a general natural gradient flow via efficient tools from numerical linear algebra. We obtain robust numerical methods for natural gradient flows without directly calculating， storing， or inverting the dense preconditioning matrix. We treat various natural gradients in a unified framework for any loss function.
Bio:
Yunan Yang is an applied mathematician working in inverse problems， optimization， and applied optimal transport. Currently， Yunan is an Advanced Fellow at the Institute for Theoretical Studies at ETH Zurich. She will be a Tenure-Track Assistant Professor in the Department of Mathematics at Cornell University starting in July 2023. Yunan Yang earned a Ph.D. degree in mathematics from the University of Texas at Austin in 2018， supervised by Prof. Bjorn Engquist. From September 2018 to August 2021， Yunan was a Courant Instructor at the Courant Institute of Mathematical Sciences， New York University.
题目：基于冷冻电镜时期的病毒非对称重构辩论
酬金东说念主：刘红荣 湖南师范大学
期间：2023年3月3日 周五上昼 10:00-11:00
所在：近春园西楼 三楼酬金厅
组织者：包承龙
选录:
病毒颗粒是一种精确的纳米机器。结构决定功能，病毒结构辩论有助于会通其入侵、复制、拼装等人命周期，同期也有助于相干的疾病防控、药物假想、疫苗开采等。多数病毒颗粒由一个二十面体对称的衣壳和衣壳里面无对称性的基因组及相干卵白构成，现存的结构辩论大多数仅限于其二十面体对称的衣壳，非对称结构想法是该限制的一大瓶颈。本酬金将先容：1）咱们发展的系列冷冻电镜病毒非对称三维重构方法；2）欺骗新方法想法的系列病毒非对称三维结构，过甚揭示病毒人命周期的相干分子机制。本酬金将触及数理科学、忖度科学、人命科学等多学科交叉的内容。
酬金东说念主：刘红荣 湖南师范大学
刘红荣，湘潭大学凝华态物理博士、好意思国加州大学洛杉矶分校博士后。现任湖南师范大学锤真金不怕火，交叉科学辩论院院长，长江学者特聘锤真金不怕火、国度“万东说念主筹画”科技编削领军东说念主才。主要从事冷冻电镜生物大分子三维重构辩论，在病毒原子结构想法、病毒对称失配与非对称重构限制作念出了系列编削竣事，相干使命发表在Science， PNAS等期刊，竣事被同业评价为“现代电子显微学的代表作”、“创举性的发现”等。当作方法矜重东说念主先后承担了国度当然科学基金要紧辩论筹画方法、重心、面上方法，国度重心研发筹画课题等科研方法。
Title: Modeling and Solving Compressible Flow with Irregular， Moving， and Colliding Geometries
Speaker: Huangrui Mo（莫晃锐）， Assistant Professor， Institute of Mechanics， Chinese Academy of Sciences
Time: 16:00-17:00， Thursday， February 23rd， 2023
Venue: Lecture Hall， Floor 3， Jin Chun Yuan West Bldg.
Organizer: Hui Yu
Abstract:
Compressible flow involving irregular， moving， and colliding geometries is an essential description of a wide range of natural and industrial flow phenomena. However， the solution of this type of flow systems is confronted with many modeling and numerical challenges. This talk introduces a numerical framework aimed at tackling related challenges. By developing a descriptive field function， an immersed boundary method treating arbitrarily irregular and moving boundaries， and a multibody contact and collision model， as well as integrating high-order temporal-spatial discretization schemes， this numerical framework is able to solve dynamic gas-solid systems ranging from subsonic to hypersonic speeds with high fidelity. This talk also shares a 3D solver established on that framework， along with its application to a set of flow cases and the automated construction of numerical wind tunnels.
Bio:
Dr. Huangrui Mo is currently an Assistant Professor in the Institute of Mechanics， CAS. He received a Bachelor， Master， and PhD from Huazhong University of Science and Technology， Institute of Mechanics， CAS， and the University of Waterloo， Canada， respectively. From 2019 to 2021， he did his postdoc at Tsinghua University. He has developed novel models and methods in the field of computational fluid dynamics and physical chemistry， with first-authored works published on journals like Int. J. Numer. Methods Fluids， J. Appl. Phys.， Shock Waves， and Phys. Chem. Chem. Phys. He also developed several influential open-source projects on Github. His current research interests include numerical methods for hypersonic flows and first-principle prediction of molecular transport coefficients.
Title: High-order bound-preserving numerical methods for chemically reacting flows
Speaker：Prof. Yang Yang (Michigan Technological University)
Time：10:00-11:00am， Dec.15th (Thur.) 2022
Venue：Zoom Meeting ID: 271 534 5558 Passcode: YMSC
https://zoom.us/j/2715345558?pwd=eXRTTExpOVg4ODFYellsNXZVVlZvQT09
Abstract:
Chemically reacting flows have many applications in combustion. There are several difficulties in constructing high-order numerical methods: (1) Due to the rapid reaction rate， the system may contain stiff source terms. (2) The transition points near the shocks may trigger the stiff source leading to spurious shock speed. (3) Physically， the density， internal energy are positive， and the mass fractions are between 0 and 1. In this talk， we discuss recent advances of high-order discontinuous Galerkin and finite difference methods for chemically reacting flows. We introduce the bound-preserving techniques for spatial derivatives. Several effective time integrations are developed. Finally， to suppress oscillations， we discuss the oscillation-free algorithm.
Bio：
In 2005， Professor Yang Yang joined the Department of Modern Mechanics at University of Science and Technology of China. One year later， he transferred to the Math Department and studied pure math， especially analysis. After receiving his Bachelor's degree of Mathematics in 2009， he started his graduate studies at Brown University and worked with Professor Chi-Wang Shu on Numerical Analysis. His work mainly focused on high order numerical methods for time-dependent problems. It includes three parts: Approximations to delta-functions， Superconvergence of discontinuous Galerkin methods and Numerical cosmology. After obtaining his Ph.D. degree in 2013， he joined the Department of Mathematical Sciences at Michigan Technological University. In 2017， he was promoted to associate professor with tenure. In 2021， he was promoted to professor with tenure.
Title: A Type of Robust Bound-preserving MUSCL-Hancock Schemes
Speaker: Chen Guoxian (Wuhan University)
Time：16:00-17:00， Dec.15th (Thur.) 2022
Venue：Tencent Meeting ID: 431642438
Join the meeting：https://meeting.tencent.com/dm/PyxmpJWoWQDb
Abstract:
The MUSCL-Hancock upwind scheme， as a variation of the MUSCL scheme， only do the initial data reconstruction once in every cell and solve the Riemann problem once at every cell interface in one time step and is widely used to solve hyperbolic conservation laws. The MMP property of the scheme with CFL number 1/2 for scalar equation is proved in [A. Suresh. SIAM Journal on Scientific Computing， 22(4):1184-1198， 2000.]. For general problems the most often cited sufficient stability conditions are obtained in [C. Berthon. Numerische Mathematik， 104(1):27-46， 2006.]: the CFL number is 1/4 of the first order scheme; the initial data reconstruction admits only the basic most dissipative minmod limiter. These contradict the faster speed and higher resolution claims of the scheme. In practice people often use robust but risk settings such as the UNO/super-bee etc. slope limiters and the CFL number up to 1/2.
In this talk we introduce new stability conditions in the sense of bound-preserving for the scheme: a) The CFL number is $(\sqrt{3}-1)/2$ which admits almost 0.73 times the time step of MUSCL scheme and then gives faster simulation; b) The preliminary reconstruction is corrected by a bound-preserving slope limiter. The slope corrector gives the bound-preserving approximation if global bound is considered， and provides a non-oscillation simulation if local bound is considered. The corrector is omitted if the well-konwn generalized minmod limiter is used for the preliminary reconstruction on scalar problem. Numerical examples verify the sharpness of these two settings and demonstrate the robustness of the schemes for advection problem with spacial variable and general nonlinear problem， we also apply the method on general nonlinear system.
Bio:
陈国贤，武汉大学数学与统计学院副锤真金不怕火，博士生导师。主要辩论自相宜出动网格方法、忖度水能源学和双曲守恒律的忖度方法等。
Title: Efficient spectral methods and error analysis for nonlinear Hamiltonian systems
Speaker：Prof. Zhang Zhimin (张智民，Wayne State University )
Time：Thur.，9:00-10:00am， Dec.8，2022
Venue：Tencent Meeting ID: 431642438
Join the meeting：https://meeting.tencent.com/dm/PyxmpJWoWQDb
Abstract:
We investigate efficient numerical methods for nonlinear Hamiltonian systems. Three polynomial spectral methods (including spectral Galerkin， Petrov-Galerkin， and collocation methods). Our main results include the energy and symplectic structure preserving properties and error estimates. We prove that the spectral Petrov-Galerkin method preserves the energy exactly and both the spectral Gauss collocation and spectral Galerkin methods are energy conserving up to spectral accuracy. While it is well known that collocation at Gauss points preserves symplectic structure， we prove that the Petrov-Galerkin method preserves the symplectic structure up to a Gauss quadrature error and the spectral Galerkin method preserves the symplectic structure to spectral accuracy. Furthermore， we prove that all three methods converge exponentially (with respect to the polynomial degree) under sufficient regularity assumption. All these aforementioned properties make our methods possible to simulate the long time behavior of the Hamiltonian system. Numerical experiments indicate that our algorithms are efficient.
Bio：
张智民：中国科技大学学士（1982）硕士（1985，导师石钟慈），马里兰大学（University of Maryland at College Park）博士 (1991，导师Ivo Babuska)； 德州理工大学（Texas Tech University ）客座助理锤真金不怕火（Visiting Assistant Professor，1991）助理锤真金不怕火（ Assistant Professor Tenure-track，1993）副锤真金不怕火（Associate Professor with tenure，1997），韦恩州立大学（Wayne State University ）副锤真金不怕火 （1999）锤真金不怕火（full Professor，2002） Charles H. Gershenson Distinguished Faculty Fellow (2014)； 栽植部长江学者讲座锤真金不怕火（中山大学，2010-2012）；担任或曾任“Mathematics of Computation“、“Journal of Scientific Computing”等9个国际忖度数学杂志编委，辩论标的是偏微分方程数值解，包括有限元，有限体积，谱方法等，发表学术论文200余篇；建议的多项式保捏重构Polynomial Preserving Recovery PPR）方法于2008年被国际上广为流行的大型交易软件 COMSOL Multiphysics 吸收，并使用于今。
Title: Moving Boundary Problems and Scientific Computing
Speaker: Dr. Shuang Liu（刘爽，University of California at San Diego）
Time：Thur.，10:00-11:00am， Dec.8，2022
Venue：Zoom Meeting ID: 271 534 5558 Passcode: YMSC
https://zoom.us/j/2715345558?pwd=eXRTTExpOVg4ODFYellsNXZVVlZvQT09
Abstract:
The moving boundary (or often called ”the free boundary”) problems arise in various mathematical models， encompassing applications that range from financial to physical and biological phenomena. However， there are challenges in the numerical study of moving boundary problems. Examples include difficulties in solving PDEs in irregular domains， handling moving boundaries efficiently and accurately， as well as computing efficiency difficulties. In this talk， I will describe our efforts in three types of moving boundary problems， with specific applications to ecology (population dynamics)， plasma physics (ITER tokamak machine design)， and cell biology (cell movement). In addition， some techniques of scientific computing will be discussed.
Bio:
Shuang Liu received a PhD in Applied and Computational Mathematics from the University of South Carolina in 2019. Previously， she was a Postdoc Research Associate in applied mathematics and plasma physics group at TDS center， Los Alamos National Laboratory， (2020-2021). Currently she is a Stefan E. Warschawski Assistant Professor in the Department of Mathematics at the University of California， San Diego. Her research interests lie in numerical partial differential equations and scientific computing， with applications in mathematical and computational biology， ecology， plasma physics， and biomolecular simulations. Especially， her research focuses on moving boundary (interface) problems using the level set method and other fast and scalable computational methods.
Title: Towards fault-tolerant quantum computation: near term and the future
Speaker: Zheng Yicong (TQL)
Time：Thur.，14:00-15:00， Dec.1st，2022
Venue：Tencent Meeting ID: 431642438
Join the meeting：https://meeting.tencent.com/dm/PyxmpJWoWQDb
Abstract:
Quantum error-correcting codes (QECCs) can eliminate the negative effects of quantum noise， the major obstacle to the execution of quantum algorithms on large-scale quantum computers. However， realizing practical quantum error correction (QEC) and fault-tolerant quantum computation (FTQC) requires resolving many challenges， both theoretical and practical. These challenges include tremendous resource overhead， low accuracy threshold， low connectivity between qubits， state leakage， et.al. In this talk， I will give a brief review of the development of the theory of fault-tolerant quantum computation and its recent developments on both theoretical and experimental sides.
Bio：
郑一聪，现任腾讯量子本质室(TQL) 量子忖度各人辩论员。在加入腾讯之前，他自2015年起在新加坡国立大学（NUS）量子时期中心（CQT）和耶鲁-国立大学学院(Yale-NUS College)作念博士后使命。他划分于2013年和2015年在南加州大学 (USC) 赢得了忖度机科学硕士学位和电子工程博士学位。他的辩论敬爱敬爱蚁合在容错量子忖度架构、量子裂缝修正/缓解、量子电路编译，量子模拟，洞开量子系统，量子忖度的物理平台（如超导量子比特、量子点和中性原子）等相干表面和本质辩论。
Title：Trace optimization and eigenvector-dependent nonlinear eigenvalue problems in data science
Speaker：Leihong Zhang（张雷洪)， Soochow University
Time：Thur.，14:00-15:00，Nov.24th，2022
Venue：Tencent Meeting ID: 431642438
Join the meeting：https://meeting.tencent.com/dm/PyxmpJWoWQDb
Abstract：
Some recent applications of multivariate statistical analysis in data science need to optimize certain trace-related objective functions over the orthogonal constraints. In this talk， we shall first present some recent applications in data science and show that solving the optimization problems can be converted to eigenvector-dependent eigenvalue problems (NEPv) for which the self-consistent filed (SCF) iteration can be effectively applied. We then discuss recent developments of the general SCF on the local convergence rate and the level-shifted technique.
Bio:
张雷洪于2008年博士毕业于香港浸会大学，现为苏州大学数学科学学院锤真金不怕火。从事最优化表面与忖度、数值线性代数、模式识别、数据挖掘等限制的辩论。主捏多项国度自科方法，参与国度当然科学基金要紧辩论筹画。在《Math Program》、《Math. Comput.》、《Numer. Math.》、《IEEE TPAMI》以及SIAM期刊系列等发表六十多篇学术论文。曾获第四届“应用数值代数奖’’、2018和2019年两届天下华东说念主数学家定约最好论文奖（若琳奖），及2019年上海市当然科学三等奖（第一完成东说念主） 等。
Title：Recent advance on Nesterov acceleration
Speaker：Bin Shi， Academy of Mathematics and Systems Science， Chinese Academy of Sciences
Time：(updated)Thur.，16:00-17:30， Nov. 24th，2022
Venue：(updated) Online Tencent ID：410-207-317
Join the meeting: https://meeting.tencent.com/dm/RpLiN266oVS7
Abstract:
Nesterov's accelerated gradient descent (NAG) is one of the milestones in the history of first-order algorithms. Until recently， it was not successfully uncovered by the high-resolution differential equation framework in [Shi et al.， 2021] that the mechanism behind the acceleration phenomenon is due to the gradient correction term. Along this way， I present some recent advances about the high-resolution differential equation framework with focusing on the implicit-velocity scheme and proximal scheme.
Bio:
史斌，本科毕业于中国海洋大学数学系，之后划分在复旦大学和好意思国麻省大学达特茅斯分校赢得基础数学和表面物理的硕士学位，于2018年在佛罗里达国际大学赢得忖度机科学的博士学位。2019年至2021年在加州大学伯克利分校跟立时器学习的前驱Michael I. Jordan从事博士后辩论使命，于2021年6月入职中国科学院数学与系统科学辩论院，任副辩论员。
Title: Deep image prior for inverse problems: acceleration and probabilistic treatment
Speaker：Bangti Jin (金邦梯)， The Chinese University of Hong Kong
Time：Mon.，14:00-15:00，Nov.21th，2022
Venue：Tencent Meeting ID: 431642438
Join the meeting：https://meeting.tencent.com/dm/PyxmpJWoWQDb
Abstract:
Since its first proposal in 2018， deep image prior has emerged as a very powerful unsupervised deep learning technique for solving inverse problems. The approach has demonstrated very encouraging empirical success in image denoising， deblurring， super-resolution etc. However， there are also several known drawbacks of the approach， notably high computational expense. In this talk， we describe some of our efforts: we propose to accelerate the training process by pretraining on synthetic dataset and further we propose a novel probabilistic treatment of deep image prior to facilitate uncertainty quantification.
Bio:
Bangti Jin received a PhD in Mathematics from the Chinese University of Hong Kong， Hong Kong in 2008. Previously， he was Lecturer and Reader， and Professor at Department of Computer Science， University College London (2014-2022)， an assistant professor of Mathematics at the University of California， Riverside (2013–2014)， a visiting assistant professor at Texas A&M University (2010–2013)， an Alexandre von Humboldt Postdoctoral Researcher at University of Bremen (2009–2010). Currently he is Professor of Mathematics at the Chinese University of Hong Kong. His research interests include inverse problems， numerical analysis and machine learning. Currently he serves on the editorial board of five journals， including Inverse Problems and Journal of Computational Mathematics.
Time：10:00-11:00， Nov. 17th (Thur.) 2022
Venue：Zoom Meeting ID: 276 366 7254 Passcode: YMSC
Zoom Link：https://zoom.us/j/2763667254?pwd=b0JoMWNBVFN4c0JXcmI0L01tblIxQT09
Title：Deep learning of multi-scale PDEs based on data generated from particle methods
Speaker：Zhongjian Wang， The University of Chicago
Abstract: Solving multiscale PDEs is difficult in high dimensional and/or convection dominant cases. The Lagrangian computation， interacting particle method， is shown to outperform solving PDEs directly (Eulerian). Examples include computing effective diffusivities， KPP front speed， and asymptotic transport properties in topological insulators. However the particle simulation takes long before convergence and does not have a continuous model. In this regard， we introduce the DeepParticle methods， which learn the pushforward map from arbitrary distribution to IPM-generated distribution by minimizing the Wasserstein distance. In particular， we formulate an iterative scheme to find the transport map and prove the convergence. On the application side， in addition to KPP invariant measures， our method can also investigate the blow-up
behavior in chemotaxis models.
Bio: Zhongjian Wang is a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago.
Time：15:30-16:30， Nov. 17th (Thur.) 2022
Venue：Tencent Meeting ID: 431642438
Join the meeting：https://meeting.tencent.com/dm/PyxmpJWoWQDb
Title：Electrically controlled self-similar evolution of viscous fingering patterns
Speaker：Meng Zhao（赵蒙)， 华中科技大学数学中心
Abstract: Interfacial instabilities are prevalent in nature and engineering. In this talk， I will discuss the dynamics of a interface in a Hele-Shaw cell under an electric field. The coupling of the hydraulic and electric fields make the dynamics of the interface very complicated. We develop an efficient algorithm to investigate the nonlinear dynamics of the interface. Our nonlinear results reveal that the electric feild plays an important in controlling the interfacial instability. Finally， we construct an efficient controlling scheme for the interface.
Bio: 赵蒙，华中科技大学数学中心副锤真金不怕火，2011年本科毕业于华东理工大学，2013和2017年在Illinois Institute of Technology， Chicago 划分赢得应用数学硕士和博士学位。2017至2021年在 University of California， Irvine担任打听助理锤真金不怕火和辩论员。2021年加入华中科技大学数学中心。主要辩论标的为Numerical Analysis， Scientific Computing (Sequential and Parallel)， Methods for Interface Problems in Materials and Fluids， Hele-Shaw Flow， Computational Fluid Mechanics and Tumor Growth。
Time：16:00-17:00， 11月10日(星期四)， Nov. 10th (Thur.) 2022
Speaker：Shuo Zhang（张硕）， LSEC， Chinese Academy of Sciences
Time：15:00-16:00， 11月3日(星期四)， Nov. 3rd (Thur.) 2022
Title：Challenges and Opportunities in Turbulent Reactive Flow Simulations
Speaker：Zhuyin Ren （任祝寅） Center for Combustion Energy /Institute of Aero Engine， Tsinghua University
Abstract: Combustion modeling is now playing an important role in the design and optimization of advanced combustion devices. For high-fidelity combustion modeling， it is essential， though challenging， to resolve the highly nonlinear turbulence-chemistry interaction (TCI) and to predict the near-limit combustion phenomena. This talk will first give a review on the grand challenges for turbulent flame simulations. The implication of stiff chemical kinetics and TCI on numerical methods will be discussed. Then the talk will discuss the potential use of machine learning in some aspects of physical modeling and computational acceleration for turbulent flame simulations. Specific examples include efficient evaluation of the nonlinear reaction mapping， the use of neural ODE for mechanism optimization， and exploring the intrinsic active subspace in uncertainty quantification.
Bio：Dr. Zhuyin Ren received his Ph.D. in Mechanical Engineering from Cornell University in 2006. He has been a Professor of the Center for Combustion Energy /Institute of Aero Engine at Tsinghua University since 2013. His research interests include turbulent combustion modeling ， numerical methods for high-fidelity engine simulations， advanced propulsion and power systems. He received the National Science Fund for Distinguished Young Scholars in 2020， and now serves as associate editors of Journal of Propulsion and Power， Combustion Theory and Modelling.
Time：16:00-17:00， 11月3日(星期四)， Nov. 3rd (Thur.) 2022
Venue：近春园西楼三层酬金厅， Lecture hall， 3rd floor of Jin Chun Yuan West Building
Title：A construction of C^r conforming finite element spaces in any dimension
Speaker：Jun Hu （胡俊）， Peking University
Abstract: This talk proposes a construction of C^r conforming finite element spaces with arbitrary r in any dimension. It is shown that if k ≥ 2^dr + 1 the space P_k of polynomials of degree ≤ k can be taken as the shape function space of Cr finite element spaces in d dimensions. This is the first work on constructing such C^r conforming finite elements in any dimension in a unified way.
Bio: 胡俊，北京大学数学科学学院党委通知、锤真金不怕火，北京大学重庆大数据辩论院院长。兼任Adv Appl. Math. Mech.延迟主编、多个期刊的编委、北京忖度数学学会理事长、中国数学会常务理事、中国大坝工程学会大坝数值模拟专委会副主任委员、重庆市工业软件应用发展协会副会长、北大-华为数学聚拢本质室主任。主要从事非方法有限元方法的辩论，尽头是弹性力常识题、线性化Einstein-Bianchi方程组及相干问题的非方法有限元方法的构造与数值分析的辩论，经管了弹性力常识题夹杂有限元方法的构造这个永远悬而未决的公开问题，初度构造了线性化Einstein-Bianchi方程组保结构的踏实有限元方法。曾获国度特出后生基金、中国忖度数学学会“首届后生编削奖”、冯康科学忖度奖等荣誉。
Title：AI-for-Science – the next wave of artificial intelligence
Speaker: Tieyan Liu (刘铁岩)
Time：16：00-17：00， Oct. 27th (Thur.) 2022
Venue：近春园西楼三楼酬金厅 Lecture Hall， Jin Chun Yuan West Bldg.; 腾讯会议：807-850-470
Abstract：
In the past decades， AI has achieved notable success in computer vision， speech recognition， and natural language understanding. However， mimicking human’s vision， speech， and language capabilities is just a shallow aspect of AI. It neglects the fact that we， as human beings， are unique because of our courage and ability to discover and change the world. AI-for-Science aims at building powerful tools to help natural scientists to better discover and change the world. Specifically， AI-for-Science assumes that the physical world can be theoretically characterized by fundamental scientific equations， usually at very large scale. It also acknowledges that there is always a gap between theory and reality， and the evidence of the gap can be found in experimental data. No one has the capability to efficiently solve all those complex scientific equations， analyze those massive experimental data， or create a closed loop between them. This is exactly where AI could play a disruptive role. As a showcase of such disruptions， I will introduce several research projects at MSR AI4Science， including Graphormer， an AI model for molecular dynamics simulation， DeepVortexNet， a neural PDE solver for fluid dynamics， SciGPT， an AI language model to automatically extract knowledge from scientific literature， and LorentzNet， and equivariant AI model to detect new particles from large-scale jet data. After introducing these works， I will also discuss some future trends of AI-for-Science research.
Bio：
刘铁岩博士，微软特出首席科学家、微软亚洲辩论院副院长、微软辩论院科学智能中心亚洲区矜重东说念主。他是国际电气电子工程师学会（IEEE）会士、 国际忖度机学会（ACM）会士、亚太东说念主工智能学会（AAIA）会士。他被聘为清华大学、香港科技大学、中国科技大学、华中科技大学兼职锤真金不怕火、诺丁汉大学荣誉锤真金不怕火。
刘博士的前卫性辩论促进了机器学习与信息检索之间的会通，被公觉得“排序学习”限制的代表东说念主物。连年来他在深度学习、强化学习、工业智能、科学智能等方面颇有成立，在顶级国际会议和期刊上发表论文数百篇，被援用数万次。他的辩论使命屡次赢得最好论文奖、最高援用论文奖、辩论冲破奖，并被庸俗应用在微软的家具和在线处事中，如必应（Bing）搜索、微软告白、Windows、Xbox、Azure等。
刘博士曾担任WWW/WebConf、SIGIR、NeurIPS、ICLR、ICML、IJCAI、AAAI、KDD、ACL等十余个国际顶级学术会议的大会主席、要领委员会主席或（资深）限制主席；ACM TOIS、ACM TWEB、IEEE TPAMI等国际期刊副主编。
他的团队于2017年开源了LightGBM，现在已成为Kaggle比赛、KDD Cup和产业方案历程中最受接待的机器学习器具之一；于2018年在中英新闻翻译任务上达到了东说念主类各人水平，并于次年赢得WMT机器翻译比赛8项冠军；于2019年研发了麻将AI Suphx，在国际知名麻将平台“天凤”上荣升十段，踏实段位显贵超过东说念主类顶级选手；2021年发布了用于分子模拟的Graphormer模子，并在KDD Cup分子建模比赛和催化剂假想洞开挑战赛中力拔头筹。
刘铁岩博士毕业于清华大学，先后赢得电子工程系学士、硕士及博士学位。
Title：Geometry and topology in collective dynamics models
Speaker：Pierre Degond， Institut de Mathématiques de Toulouse CNRS & Université Paul Sabatier
Time：15:30-16:30， 10月20日(星期四)， Oct. 20th (Thur.) 2022
Zoom Meeting ID: 276 366 7254 Passcode: YMSC
Zoom Link： https://zoom.us/j/2763667254?pwd=b0JoMWNBVFN4c0JXcmI0L01tblIxQT09
Abstract: Collective dynamics arises in systems of self-propelled particles and plays an important role in life sciences， from collectively migrating cells in an embryo to flocking birds or schooling fish. It has stimulated intense mathematical research in the last decade. Many different models have been proposed but most of them rely on point particles. In practice， particles often have more complex geometrical structures. Here， we will consider particles as rigid bodies whose body attitude is described by an orthonormal frame. Particles tend to align their frame with those of their neighbours. A hydrodynamic model will be derived when the number of particles is large. It will be used to exhibit solutions having non-trivial topology. We will investigate whether topology provides enhanced stability against perturbations， as observed in other systems such as topological insulators. This talk is based on recent results issued from collaborations with Antoine Diez， Amic Frouvelle， Sara Merino-Aceituno， Mingye Na and Ariane Trescases.
Bio: Prof. Degond was trained at the Ecole Normale Supérieure in Paris and his first appointment was in Ecole Polytechnique in Palaiseau in 1985 as a Junior Researcher at CNRS. He was then appointed a full Professor in Ecole Normale Superieure of Cachan in 1990. He joined back the CNRS in Toulouse as a Senior Researcher in 1993， where he founded the Applied Math group， and holds a permanent position. He has been a Chair Professor in Applied Mathematics at Imperial College in the period 2013-2020， and a Visiting Professor in Mathematics afterwards. He is interested in plasma physics， rarefied gas dynamics， semiconductor modeling， collective dynamics， decision making and self-organization in complex systems arising from biology and social sciences. His methods combine analysis， asymptotic theory and multiscale numerical techniques. He has been been an invited speaker at the 2018 International Congress of Mathematicians (ICM 2018). He was awarded the Jacques-Louis Lions prize 2013 of the French Academy of Sciences and a Royal Society Wolfson Research Merit Award holder in 2014-2018.
Time：15:30-16:30， Oct. 13th (Thur.) 2022
Venue：近春园西楼三层酬金厅， Lecture hall， 3rd floor of Jin Chun Yuan West Building
Title：Computing quantum dynamics: towards fighting against multiscales and high dimensionality
Speaker：Zhennan Zhou （周珍楠）， Peking University
Abstract: We develop a Monte Carlo algorithm named the Frozen Gaussian Sampling (FGS) to solve the semiclassical Schrodinger equation based on the frozen Gaussian approximation. Due to the highly oscillatory structure of the wave function， traditional mesh-based algorithms suffer from ”the curse of dimensionality”， which gives rise to more severe computational burden when the semiclassical parameter ε is small. The Frozen Gaussian sampling outperforms the existing algorithms in that it is mesh-free in computing the physical observables and is suitable for high dimensional problems. We also discussion the extension of the FGS approach to the mixed quantum-classical dynamical models.
Bio: 周珍楠，北京大学北京国际数学辩论中心助理锤真金不怕火、博士生导师。2014 年在好意思国威斯康辛大学麦迪逊分校赢得博士学位，2014-2017 年在好意思国杜克大学担任助理辩论锤真金不怕火，2017 年加入北京大学北京国际数学辩论中心。主要辩论限制为微分方程的应用分析，微分方程数值解，应用立时候析，立时模拟等，尽头是宽恕起首于当然科学的应用数常识题。入选中组部第十四批“千东说念主筹画”后生东说念主才方法（2018）。
Title: Learning for the Future Power Grid
Speaker：Chenye Wu (吴辰晔)， CUHK (Shenzhen)
Time：16:00-17:15， Oct. 6th (Thur.) 2022
Tencent: 511 466 354
Abstract: Advanced learning frameworks are reshaping the landscape of power grid operation and the electricity market design.This talk shares two stories， both of which seek to use learning frameworks to enhance the future power grid. The first one investigates the storage control problem for consumers. Specifically， we consider that consumers face dynamic electricity prices and seek to use storage to reduce their electricity bills. The challenges come from the uncertainty in the electricity price and consumers' demand.We propose a practical learning-based online storage control policy. The second story studies a classical procedure in the electricity market，the economic dispatch problem， i.e.， matching the electricity supply and demand at the minimal generation cost. The critical challenge is again from the uncertainty in the system demand. Hence， the conventional approach is to conduct the dispatch based on predicted demand.However， we submit that this conventional approach can be suboptimal， and we propose a model-free algorithm for economic dispatch based on the end-to-end learning framework.
Bio: Dr. Chenye Wu is currently an Assitant Professor and the presidential young fellow at the School of Science and Engineering， The Chinese University of Hong Kong， Shenzhen. Dr. Wu received his bachelor's degree in electronic engineering from Tsinghua University in 2009 and his Ph.D. degree in computer science and engineering from Tsinghua University in 2013， advised by Prof. Andrew Yao， the Turing Award Laurant. Dr. Wu's research interests span from power system control to the electricity market design， emphasizing the emerging business model design for the energy sector， the market power analysis for the electricity market， the AI-driven power system control and operation. Dr. Wu has published over 70 research articles in top journals and leading conferences in the field， including IEEE Transactions on Power Systems， IEEE Transactions on Smart Grid， IEEE Transactions on Sustainable Energy， ACM e-Energy. He is a member of the FinTech special interest group， China Society for Industrial and Applied Mathematics， and a member of the special interest group， China Energy Society. Dr. Wu has been an Editorial Board Member for IEEE Systems Journal as an Associate Editor since February 2022. He is the symposium co-Chair for IEEE SmartGridComm 2022 and the digital conference co-Chair for ACM e-Energy 2022. Dr. Wu is the co-recipients of the three best paper awards， including the best paper award for IEEE SmartGridComm 2012 and IEEE PES General Meeting 2013 and 2020.
期间 Time：14:00-15:30， 9月30日(星期五)， Sep. 30th (Fri.) 2022
所在 Venue：近春园西楼三层酬金厅， Lecture hall， 3rd floor of Jin Chun Yuan West Building
Title： Modulated Free Energy and Mean Field Limit
Speaker: Zhenfu Wang (王振富)， 北京大学北京国际数学辩论中心 Beijing International Center for Mathematical Research， Peking University
Abstract: We prove the mean field limit and quantitative estimates for many-particle systems with singular attractive interactions between particles. As an important example， a full rigorous derivation (with quantitative estimates) of the Patlak-Keller-Segel model in optimal subcritical regimes is obtained for the first time. To give an answer to this longstanding problem， we take advantage of a new modulated free energy and we prove some precise large deviation estimates encoding the competition between diffusion and attraction. This modulated free energy approach can also treat the systems with a wide range of repulsive kernels， including the vanishing viscosity case. Based on joint works with D. Bresch and P.-E. Jabin.
个东说念主简介：王振富，2012年本科毕业于南京大学，2017年获好意思国马里兰大学数学博士学位，博士导师为 Pierre-Emmanuel Jabin。2017年7月到2020年6月在好意思国宾夕法尼亚大学从事博士后辩论使命。2020年10月入职北京大学，现任北京国际数学辩论中心助理锤真金不怕火、辩论员。主要辩论限制为交互粒子系统的平均场极限和动理学方程的分析。
期间 Time：16:00-17:00， 9月29日(星期四)， Sep. 29th (Thur.) 2022
所在 Venue：近春园西楼三层酬金厅， Lecture hall， 3rd floor of Jin Chun Yuan West Building
Title： On splitting methods for the Dirac equation in the nonrelativistic limit regime
Speaker：Yongyong Cai (蔡勇勇)， School of Mathematical Sciences， Beijing Normal University(北京师范大学)
Abstract: We establish error bounds of the Lie-Trotter splitting and Strang splitting for the Dirac equation in the nonrelativistic limit regime in the absence of external magnetic potentials. In this regime， the solution admits high frequency waves in time. Surprisingly， we find out that the splitting methods exhibit super-resolutions， i.e. the methods can capture the solutions accurately even if the time step size is much larger than the sampled wavelength. Lie splitting shows half order uniform convergence w.r.t temporal wave length. Moreover， if the time step size is non-resonant， Lie splitting would yield an improved uniform first order uniform error bound. In addition， we show Strang splitting is uniformly convergent with half order rate for general time step size and uniformly convergent with three half order rate for non-resonant time step size. We also discuss the case with external magnetic potentials， and splitting schemes also show superior performance among the commonly used numerical methods.
个东说念主简介：蔡勇勇，北京师范大学锤真金不怕火，本科和硕士就读于北京大学，2012年在新加坡国立大学赢得博士学位。他先后在威斯康辛大学麦迪逊分校、马里兰大学帕克分校和普渡大学从事博士后辩论使命，从2016年至2019年在北京忖度科学辩论中心任特聘辩论员。蔡勇勇博士的辩论敬爱敬爱主如果偏微分方程的数值方法过甚在量子力学等限制中的应用。
题目：Error statistics and scalability of quantum error mitigation formulasOrganizer / 组织者：魏朝日Speaker / 主讲东说念主：Xiaodie Lin（清华大学）Time / 期间：15:00-16:00pm， September 22 (Thur.) 2022Venue / 所在：Ningzhai 宁斋S11
选录：Quantum error mitigation is crucial for us to protect quantum computing against quantum errors before quantum error correction is truly available， which is still one or two decades away. Though some error mitigation protocols， like error extrapolation and error cancellation， have been demonstrated successfully in experiments using small scale quantum systems， whether they behave well on large scale quantum computers remains unclear. Recently， it has been found out that after error mitigation， the remaining error is roughly of order the square root of N， where N is the number of quantum gate number.
期间 Time：16:30-17:30， 9月22日(星期四)， Sep. 22th (Thur.) 2022
所在 Venue：近春园西楼三层酬金厅， Lecture hall， 3rd floor of Jin Chun Yuan West Building
Title：Optimization， Generalization and Implicit bias of Gradient Methods in Deep Learning
Speaker：Jian Li (李建)， Institute for Interdisciplinary Information Sciences (IIIS)， Tsinghua University.
Abstract: Deep learning has enjoyed huge empirical success in recent years. Although training a deep neural network is a highly nonconvex optimization problem，
simple (stochastic) gradient methods are able to produce good solutions that minimize the training error， and more surprisingly， can generalize well to out-of sample data， even when the number of parameters is significantly larger than the amount of training data. It is known that the optimization algorithms (various gradient-based methods) contribute greatly to the generalization properties of deep learning. However， recently， researchers have found that gradient methods (even gradient descent) may not converge to a stationary point， the loss graduately decreases but not necessarily monotonically， and the sharpness of the loss landscape (i.e.， the max eigenvalue of the Hessian) may oscillate， entering a regime called edge of stability. These behaviors are inconsistent with several classical presumptions widely studied in the field of optimization. Moreover， what bias is introduced by the gradient-based algorithms in neural network training? What characteristics of the training ensures good generalization in deep learning? In this talk， we investigate these question from the perspective of the gradient based optimization methods. In particular， we attempt to explain some of the behaviors of the optimization trajectory (e.g.， edge of stability)， prove new generalization bounds and investigate the implicit bias of various gradient methods.
Bio：Jian Li is currently a tenured associate professor at Institute for Interdisciplinary Information Sciences (IIIS)， Tsinghua University， headed by Prof. Andrew Yao. He got his BSc degree from Sun Yat-sen (Zhongshan) University， China， MSc degree in computer science from Fudan University， China and PhD degree in the University of Maryland， USA. His major research interests lie in theoretical computer science， machine learning， databases and finance. He co-authored several research papers that have been published in major computer science conferences and journals. He received the best paper awards at VLDB 2009 and ESA 2010， best newcomer award at ICDT 2017.
Title：Computational Quantum Mechanics in Phase Space — An Attempt to Break the Curse of Dimensionality
Speaker：Sihong Shao (邵嗣烘) ，School of Mathematical Sciences， Peking University， sihong@math.pku.edu.cn
Time: 14:30-15:30， September 16th(Fri.) 2022
Venue：近春园西楼三层酬金厅
Abstract：
The Wigner function has provided an equivalent and convenient way to render quantum mechanics in phase space. It allows one to express macroscopically measurable quantities， such as currents and heat fluxes， in statistical forms as usually does in classical statistical mechanics， thereby facilitating its applications in nanoelectronics， quantum optics and etc. Distinct from the Schrödinger equation， the most appealing feature of the Wigner equation， which governs the dynamics of the Wigner function， is that it shares many analogies to the classical mechanism and simply reduces to the classical counterpart when the reduced Planck constant vanishes. Despite the theoretical advantages， numerical resolutions for the Wigner equation is notoriously difficult and remains one of the most challenging problems in computational physics， mainly because of the high dimensionality and nonlocal pseudo-differential operator. On one hand， the commonly used finite difference methods fail to capture the highly oscillatory structure accurately. On the other hand， all existing stochastic algorithms， including the affinity-based Wigner Monte Carlo and signed particle Wigner Monte Carlo methods， have been confined to 2D phase space. Few results have been reported for higher dimensional simulations. My group has made substantial progress in both aspects.
We attempted to solve the Wigner equation in 4-D and 6-D phase space with gird-based deterministic methods by exploiting its intriguing mathematical structure. For 4-D simulations， we succeeded to detail the quantum dynamics of a Helium-like system and the quantum interference fringes in the double-slit experiment. For the 6-D Wigner-Coulomb system， we proposed a massively parallel solver， termed the characteristic-spectral-mixed scheme (CHASM)， which utilizes the locally distributed cubic B-spline basis to interpolate the local spatial advection and the truncated kernel method to approximate the pseudodifferential operator with weakly singular symbol under the Coulomb interaction. Several typical numerical experiments demonstrate the accuracy and efficiency of CHASM， as well as its scalability up to 16000 cores.
On the other hand， we built the bridge between the Wigner equation and a stochastic particle method in a rigorous manner and proposed a SPA (Stationary Phase Approximation) + SPADE (Sequential-clustering Particle Annihilation via Discrepancy Estimation) strategy to overcome the sign problem where the curse of dimensionality which causes the unattainable exponential wall is translated into the NP-hard problems that may have approximate solutions. SPADE follows a divide-and-conquer strategy: Adaptive clustering of particles via controlling their number-theoretic discrepancies and independent random matching in each group， and it may learn the minimal amount of particles that can accurately capture the non-classicality of the Wigner function. A thorough performance benchmark of SPADE is provided with the reference solutions in 6-D phase space produced by CHASM under a 73^3*80^3 uniform grid， which fully explores the limit of grid-based deterministic Wigner solvers. Simulations of the proton-electron couplings in 6-D and 12-D phase space demonstrate the accuracy and the efficiency of our particle-based stochastic methods.
As a permanent goal and a tireless direction of computational mathematics， developing an accurate and stable high-dimensional solver has been attracting more and more attentions in recent years due to the urgent need in e.g.， quantum science and high energy density physics. This talk represents our recent attempts to break the curse of dimensionality which poses a fundamental obstacle to high-dimensional numerical simulations.
邵嗣烘，北京大学数学科学学院副锤真金不怕火，毕业于北京大学数学科学学院并赢得理学学士和博士学位，先后到访过北卡罗莱那大学夏洛特分校，香港科技大学，普林斯顿大学、塞维利亚大学和香港汉文大学等。主要开展面向智能、量子和忖度的交叉会通辩论，落脚点在基础的数学表面和高效的算法假想，强调破碎数学结构的假想、分析和应用。具体辩论限制包括：高维问题的数值方法、组合优化、忖度量子力学、图（汇集）上的数学过甚算法、微分方程数值解和脑科学等，获国度当然科学基金后生，面上和优青联结资助。2019年入选北京智源东说念主工智能辩论院“智源后生科学家”。2020年获北京大学优秀博士学位论文迷惑敦厚。2021年获北京大学黄廷芳/信和后生特出学者奖。曾获中国忖度数学学会优秀后生论文一等奖，北京大学学术类编削奖，北京大学优秀博士学位论文三等奖，宝洁教师奖和北京大学优秀班主任等。
Title/题目：Universal cost bound of quantum error mitigation based on quantum estimation theory
Organizer/组织者：魏朝日
Speaker/主讲东说念主：Weixiao Sun（清华大学）
Time/期间：15:00-16:00pm， September 8th(Thur.) 2022
Venue/所在：Ningzhai 宁斋S11；Tencent Meeting ID: 235-622-864
Abstract/选录：Quantum error mitigation is very important for us to protect quantum computing from errors before we have sufficient computational resources to apply quantum error correction. Though quite a few techniques have been proposed for this purpose， little is known about their fundamental aspects， say the limitation of their power. Recently， a new approach that analyzes the cost of quantum error mitigation using the quantum estimation theory has been proposed， where by proving that the quantum Fisher information decays exponentially with the circuit depth， it has been shown that unbiased estimation of an observable encounters an exponential growth in the lower bound on the measurement cost， or more precisely the required number of copies of noisy quantum state.
题目：重念念遥感图像收复的基本方法论
酬金东说念主：孟德宇 （西安交通大学）
期间：2022/08/26 10:00-11:30am
#腾讯会议：521-250-349
选录：针对遥感图像收复问题，传统方法论主要分为模子开动与数据开动两类。其中模子开动主要通过意识数据，事先假想合理的蚀本与正则项，从而达到纯粹收复效果。而数据开动主要通过鉴戒忖度机视觉限制通用有用深度汇集的构建技能，通过端到端机器学习的方式来赢得针对退化遥感图像的显式收复函数，从而便于泛化使用。然则，针对遥感图像的额外内涵，两种方法论均存在内在的颓势。本酬金中，将尝试对已有底层遥感图像时期进行内在功能的分析，从而反念念其局限性，进而经营怎样对遥感图像简略愈加合理假想方法论的可能计谋。
Title: Data-driven computational multiscale methods and applications
Speaker: Eric T. Chung (The Chinese University of Hong Kong)
Time: 2022/07/19， 10am-11am
Venue：Lecture hall， 3rd floor of Jin Chun Yuan West Building；Tencent Meeting: 502-4821-2807
Organizer: Jie Du
Abstract: Many practical problems， especially those arising from geosciences， have multiscale features due to medium heterogeneities， nonlinearity and coupling of multiple models. The goal of multiscale methods or numerical upscaling techniques is to compute the solutions of these complicated problems efficiently by constructing coarse scale equations for some dominant components of the solutions. In this talk， we will present the latest development of a class of multiscale methods， which make use of solutions of local problems to obtain coarse scale equations and have rigorous convergence theories. For nonlinear problems， the macroscopic parameters in the coarse scale equations can be computed efficiently by the use of deep learning techniques. We will discuss the general concepts and present some applications.
Bio: Eric T. Chung is a Professor in the Department of Mathematics in The Chinese University of Hong Kong. He obtained PhD degree from University of California at Los Angeles. His Ph.D. thesis advisor is Prof. Bjorn Engquist. His research interests are Discontinuous Galerkin Methods， Computational Wave Propagation， Fluid Flow in Heterogeneous Media， Multiscale Model Reduction Techniques， Adaptivity for Multiscale Problems， Domain Decomposition Methods， Seismic Imaging and Travel Time Tomography.
