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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > BIMSA-Tsinghua Seminar on Machine Learning and Differential Equations Data-Driven Modeling and Learning Using the Weak Form
Data-Driven Modeling and Learning Using the Weak Form
Organizers
Fan Sheng Xiong , Wu Yue Yang , Wen An Yong , Yi Zhu
Speaker
David Bortz
Time
Thursday, October 20, 2022 8:50 AM - 12:15 PM
Venue
1129B
Online
Zoom 537 192 5549 (BIMSA)
Abstract
Data-driven modeling approaches have proven highly successful in a wide range of fields in science and engineering. In this talk, I will discuss several ubiquitous challenges with the conventional model development / discretization / parameter inference / model revision loop (illustrating the issues with examples from Microbial Electrolysis Cells, chemotaxis, and HIV viral dynamics). I will present our Weak form Sparse Identification of Nonlinear Dynamics (WSINDy) framework which addresses several of these challenges and offers substantial advantages in terms of computational efficiency, noise robustness, and modest data needs (in an online learning context). Lastly, I will illustrate applications of WSINDy to several benchmark problems as well as to the inference of interacting particle systems, specifically learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population.
Speaker Intro
Prof. Bortz earned his PhD in 2002 with H.T. Banks at North Carolina State University. He was then a postdoc at the University of Michigan in Mathematics and joined the faculty in Applied Math at the University of Colorado in 2006. The core of his research interests are in scientific computation methodologies for data-driven modeling and inverse problems at the intersection of applied math and statistics. His research has received support from NSF, NIH, DOE, DOD, and NVIDIA.
Beijing Institute of Mathematical Sciences and Applications
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