AMS Sectional Meeting AMS Special Session
Current as of Sunday, April 14, 2024 03:30:04
2024 Spring Eastern Sectional Meeting
- Howard University, Washington, DC
- April 6-7, 2024 (Saturday - Sunday)
- Meeting #1194
Associate Secretary for the AMS Scientific Program:
Steven H Weintraub, Lehigh University shw2@lehigh.edu
Special Session on Recent Advances on Machine Learning Methods for Forward and Inverse Problems
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Saturday April 6, 2024, 8:00 a.m.-10:00 a.m.
Special Session on Recent Advances on Machine Learning Methods for Forward and Inverse Problems, I
Discover the latest innovations at the intersection of mathematics and machine learning in our special session, "Recent Advances on Machine Learning Methods for Forward and Inverse Problems." Join experts from diverse fields as they unveil novel theories and algorithms addressing forward and inverse problems through cutting-edge machine learning techniques. Explore the fusion of physics-based models with data-driven approaches, and delve into uncertainty quantification, randomized sampling, and real-world applications spanning medical imaging, fluid dynamics and more. Embrace this opportunity to engage with forefront research, foster interdisciplinary collaborations, and shape the future of problem-solving at the Spring 2024 AMS eastern sectional meeting at Howard University.
LKH 365, Alain Locke Hall
Organizers:
Haizhao Yang, University of Maryland College Park hzyang@umd.edu
Ke Chen, University of Maryland, College Park
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8:00 a.m.
Finite Expression Method: A Symbolic Approach for Scientific Machine Learning
Zhongyi Jiang, University of Delaware
Senwei Liang, Purdue University
Zezheng Song, University of Maryland, College Park
Chunmei Wang, University of Florida
Haizhao Yang*, University of Maryland College Park
(1194-65-35039) -
9:00 a.m.
An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems
Zezhong Zhang*, Oak Ridge National Laboratory
(1194-65-34983)
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8:00 a.m.
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Saturday April 6, 2024, 3:00 p.m.-5:00 p.m.
Special Session on Recent Advances on Machine Learning Methods for Forward and Inverse Problems, II
Discover the latest innovations at the intersection of mathematics and machine learning in our special session, "Recent Advances on Machine Learning Methods for Forward and Inverse Problems." Join experts from diverse fields as they unveil novel theories and algorithms addressing forward and inverse problems through cutting-edge machine learning techniques. Explore the fusion of physics-based models with data-driven approaches, and delve into uncertainty quantification, randomized sampling, and real-world applications spanning medical imaging, fluid dynamics and more. Embrace this opportunity to engage with forefront research, foster interdisciplinary collaborations, and shape the future of problem-solving at the Spring 2024 AMS eastern sectional meeting at Howard University.
LKH 365, Alain Locke Hall
Organizers:
Haizhao Yang, University of Maryland College Park hzyang@umd.edu
Ke Chen, University of Maryland, College Park
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3:00 p.m.
Algorithm Design and Approximation Using DNNs
Harbir Antil*, George Mason University
(1194-49-35209) -
4:00 p.m.
A deep learning method for the dynamics of classic and conservative Allen-Cahn equations based on fully-discrete operators
Yuwei Geng*, Yuankai Teng
(1194-35-34845)
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3:00 p.m.
Inquiries: meet@ams.org