Applied/ACMS

Applied and Computational Mathematics Seminar

• When: Fridays at 2:25pm (except as otherwise indicated)
• Where: 901 Van Vleck Hall
• Organizers: Saverio Spagnolie, Chris Rycroft, and Laurel Ohm
• To join the ACMS mailing list: Send mail to acms+subscribe@g-groups.wisc.edu.

Fall 2025

Date	Speaker	Title	Host(s)
Sep 19*	Zichao (Wendy) Di (Argonne National Laboratory)	Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science	Rycroft/Li
Sep 26	Pouria Behnoudfar (UW)	Bridging Conceptual and Operational Models: An Explainable AI Framework for Next-Generation Climate Emulators	Spagnolie
Oct 3
Oct 10*	Alexandria Volkening (Purdue)	TBD	Rycroft
Oct 17*	Nick Derr (UW)	TBD	Spagnolie
Oct 24	Mike O'Neil (Courant)	TBD	Spagnolie
Oct 31	Hyukpyo Hong (UW)	TBD	Spagnolie
Nov 7*	John Bush (MIT)	TBD	Spagnolie
Nov 14	Yukun Yue (UW)	TBD	Spagnolie
Nov 21*	Jessie Levillain (CNES/INSA Toulouse)	TBD	Ohm
Nov 28	Thanksgiving
Dec 5	Jiamian Hu (UW)	TBD	Chen
Dec 12	Thomas Fai (Brandeis)	TBD	Rycroft

[Dates marked with an asterisk are close to weekends with a home game for the UW Badgers football team. Hotel availability around these dates is often limited if booked on short notice.]

Abstract

Zichao (Wendy) Di (Argonne National Laboratory)

Title: Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science

X-ray imaging experiments generate vast datasets that are often incomplete or ill-posed when considered in isolation. One way forward is multimodal data analysis, where complementary measurement modalities are fused to reduce ambiguity and improve reconstructions. A key question, both mathematically and practically, is how to identify which modalities to combine and how best to integrate them within an inverse problem framework.

A second line of work focuses on the computational challenge: even for single-modality inverse problems, the resulting optimization problems are large-scale, nonlinear, and nonconvex. Here, I will discuss multilevel optimization and stochastic sampling strategies that accelerate convergence by exploiting hierarchical structure in both parameter and data spaces.

Although developed separately, these two directions point toward a common goal: building scalable, optimization-based frameworks that make the best use of diverse data to enable new discoveries in X-ray imaging science.

Pouria Behnoudfar (UW Madison)

Title: Bridging Conceptual and Operational Models: An Explainable AI Framework for Next-Generation Climate Emulators

Computer models are indispensable tools for understanding and predicting the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, particularly in simulating extreme events and statistical distributions. In contrast, coarse-grained conceptual models isolate fundamental processes and can be precisely calibrated to excel in characterizing specific dynamical and statistical features. Yet, different models often operate independently. By leveraging the complementary strengths of models of varying complexity, we develop a robust, explainable AI framework as a next-generation climate emulator. It bridges the model hierarchy through a reconfigured latent space data assimilation technique, uniquely suited to optimally exploit the sparse output from the conceptual models. The resulting bridging model inherits the high resolution and comprehensive variables of operational models while achieving global accuracy enhancements through targeted improvements from simpler models. Crucially, the AI's mechanism of inter-model communication provides a clear rationale for why each part of the bridging model is improved, moving beyond black-box correction to physically insightful understanding. This computationally efficient framework enables the creation of high-quality digital twins and advances uncertainty quantification for extreme events. We demonstrate its power by significantly correcting biases in CMIP6 simulations of El Ni\~no complexity using simpler, statistically accurate conceptual models.

Archived semesters

• Spring 2025
• Fall 2024
• Spring 2024
• Fall 2023
• Spring 2023
• Fall 2022
• Spring 2022
• Fall 2021
• Spring 2021
• Fall 2020
• Spring 2020
• Fall 2019
• Spring 2019
• Fall 2018
• Spring 2018
• Fall 2017
• Spring 2017
• Fall 2016
• Spring 2016
• Fall 2015
• Spring 2015
• Fall 2014
• Spring 2014
• Fall 2013
• Spring 2013
• Fall 2012
• Spring 2012
• Fall 2011
• Spring 2011
• Fall 2010

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