CAM Seminar

Computational and Applied Mathematics Seminar

Spring 2025

Mondays at 2:15-3:05p.m  (in-person (Room Carver 401) or ZOOM/WebEx talks)

The CAM Seminar is organized in the ISU Mathematics Department. It brings speakers from inside and outside of ISU, raising issues and exchanging ideas on topics of current interest in the area of computational and applied mathematics.

 


  • February 03

       Title:   

Abstract:   


  • February 10 (Webex Meeting)

       Title:   Generalized Schrödinger Bridges in Learning and Control

        Abhishek Halder,  Aerospace Engineering, Iowa State University

Abstract:   A bridge is a diffusion process that connects a given state to another within a given deadline. Well known examples include Brownian bridge and Bessel bridge. Schrödinger bridge is a diffusion process that connects a given distribution to another within a given deadline. Schrödinger bridge comes with a maximum likelihood guarantee in the path space subject to the endpoint constraints. It was conceived as a thought experiment by physicist Erwin Schrödinger in 1931-32 with the motivation of explaining quantum mechanics through the lens of stochastic processes. The topic sits at the crossroad of stochastic optimal control and ML, and in recent years have made rapid inroads to diffusion models for generative AI. This talk will summarize recent works on generalized variants of the Schrödinger bridge, and the applications motivating these ideas. We will explain how these developments happening in control theory and ML are making new points of contact with quantum mechanics.

Webex Meeting link:
https://iastate.webex.com/iastate/j.php?MTID=m88bb7621237b386073241f080…

Meeting number:
2863 760 9120

Meeting password:
1111

Join from a video or application
Dial 28637609120@iastate.webex.com
You can also dial 173.243.2.68 and enter your meeting number.

Join by phone
+1-312-535-8110 Toll
Access code: 28637609120


Global call-in numbers
https://iastate.webex.com/iastate/globalcallin.php?MTID=m897e530284614a…

 


  • February 17

       Title:   

Abstract:   


  • February 24

       Title:   

Abstract:   


  • March 03

       Title:   

Abstract:   


  • March 10

       Title:   

       James Lambers, University of Southern Mississippi

Abstract:   


  • March 24

       Title:   

Abstract:   


  • March 31

       Title:   Finite Expression Method: A Symbolic Approach for Scientific Machine Learning 

       Haizhao Yang, University of Maryland College Park

Abstract:   Machine learning has revolutionized computational science and engineering with impressive breakthroughs, e.g., making the efficient solution of high-dimensional computational tasks feasible and advancing domain knowledge via scientific data mining. This leads to an emerging field called scientific machine learning. In this talk, we introduce a new method for a symbolic approach to solving scientific machine learning problems. This method seeks interpretable learning outcomes via combinatorial optimization in the space of functions with finitely many analytic expressions and, hence, this methodology is named the finite expression method (FEX). It is proved in approximation theory that FEX can efficiently learn high-dimensional complex functionss. As a proof of concept, a deep reinforcement learning method is proposed to implement FEX for learning the solution of high-dimensional PDEs and learning the governing equations of raw data.


  • April 07

       Title:   

Abstract:   


  • April 14

       Title:   

Abstract:   


  • April 21

       Title:   

Abstract:   


  • April 28

       Title:   

Abstract: