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*'''When:''' Fridays at 2:25pm (except as otherwise indicated)
*'''When:''' Fridays at 2:25pm (except as otherwise indicated)
*'''Where:''' 901 Van Vleck Hall
*'''Where:''' 901 Van Vleck Hall
*'''Organizers:''' [https://math.wisc.edu/staff/fabien-maurice/ Maurice Fabien], [https://people.math.wisc.edu/~rycroft/ Chris Rycroft], and [https://www.math.wisc.edu/~spagnolie/ Saverio Spagnolie],
*'''Organizers:''' [https://www.math.wisc.edu/~spagnolie/ Saverio Spagnolie], [https://people.math.wisc.edu/~rycroft/ Chris Rycroft], and [https://sites.google.com/view/laurel-ohm-math Laurel Ohm]  
*'''To join the ACMS mailing list:''' Send mail to [mailto:acms+join@g-groups.wisc.edu acms+subscribe@g-groups.wisc.edu].
*'''To join the ACMS mailing list:''' Send mail to [mailto:acms+join@g-groups.wisc.edu acms+subscribe@g-groups.wisc.edu].


<br>   
<br>   


== '''Spring 2025''' ==
== '''Fall 2025''' ==
{| cellpadding="8"
{| cellpadding="8"
! align="left" |Date
! align="left" |Date
Line 17: Line 17:
! align="left" |Host(s)
! align="left" |Host(s)
|-
|-
|Jan 31
|Sep 19*
|[https://people.math.wisc.edu/~tgchandler/ Thomas Chandler] (UW)
|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory)
|[[#Chandler|''Fluid–structure interactions in active complex fluids'']]
|[[#Di|Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science]]
|
|Rycroft/Li
|-
|-
|Feb 7
|Sep 26
|[https://www.colorado.edu/aps/adrian-fraser Adrian Fraser] (Colorado)
|[https://scholar.google.com/citations?user=Imuw5CMAAAAJ&hl=en&oi=ao Pouria Behnoudfar] (UW)
|[[#Fraser|''Destabilization of transverse waves by periodic shear flows'']]
|[[#Behnoudfar|Bridging Conceptual and Operational Models: An Explainable AI Framework for Next-Generation Climate Emulators]]
|Spagnolie
|Spagnolie
|-
|-
|Feb 14
|Oct 3
|TBA
|
|[https://jrluedtke.github.io/ Jim Luedtke] (UW)
|[[#Luedtke|TBA]]
|Spagnolie
|-
|Feb 21
|TBA
|
|
|
|
|-
|-
|Feb 28
|Oct 10*
|[https://nmboffi.github.io/ Nick Boffi] (CMU)
|[https://www.alexandriavolkening.com Alexandria Volkening] (Purdue)
|[[#Boffi|TBA]]
|TBD
|Li
|Rycroft
|-
|-
|Mar 7
|Oct 17*
|[https://sites.lsa.umich.edu/shankar-lab/ Suraj Shankar] (Michigan)
|[https://www.nickderr.me/ Nick Derr] (UW)
|[[#Shankar|TBA]]
|TBD
|Spagnolie
|Spagnolie
|-
|-
|Mar 14
|Oct 24
|[https://lu.seas.harvard.edu/ Yue Lu] (Harvard) '''[Colloquium]'''
|[https://cims.nyu.edu/~oneil/ Mike O'Neil] (Courant)
|[[#Lu|TBA]]
|TBD
|Li
|Spagnolie
|-
|-
|Mar 21
|Oct 31
|[https://people.llnl.gov/vogman1 Genia Vogman] (LLNL)
|[https://people.math.wisc.edu/~hhong78/ Hyukpyo Hong] (UW)
|[[#Vogman|TBA]]
|TBD
|Li
|Spagnolie
|-
|Nov 7*
|[https://thales.mit.edu/bush/ John Bush] (MIT)
|TBD
|Spagnolie
|-
|-
|Mar 28
|Nov 14
|''Spring Break''
|[https://sites.google.com/andrew.cmu.edu/yukunyue/home Yukun Yue] (UW)
|
|TBD
|
|Spagnolie
|-
|Nov 21*
|[https://jesnial.github.io/ Jessie Levillain] (CNES/INSA Toulouse)
|TBD
|Ohm
|-
|-
|Apr 4
|Nov 28
|TBA
|Thanksgiving
|
|
|
|
|-
|-
|Apr 11
|Dec 5
|[https://meche.mit.edu/people/faculty/pierrel@mit.edu Pierre Lermusiaux] (MIT)
|[https://mesomod.weebly.com/ Jiamian Hu] (UW)
|[[#Lermusiaux|TBA]]
|TBD
|Chen
|Chen
|-
|-
|Apr 18
|Dec 12
|[https://www.math.uci.edu/~jxin/ Jack Xin] (UC Irvine) '''[Colloquium]'''
|[https://sites.google.com/a/brandeis.edu/tfai/home Thomas Fai] (Brandeis)
|[[#Xin|TBA]]
|TBD
|
|Rycroft
|-
|Apr 25
|[https://www-users.cse.umn.edu/~bcockbur/ Bernardo Cockburn] (Minnesota)
|[[#Cockburn|''Transforming stabilization into spaces'']]
| Stechmann, Fabien
|-
|May 2
|[https://sylviaherbert.com/ Sylvia Herbert] (UCSD)
|[[#Herbert|TBA]]
|Chen
|}
|}
''[Dates marked with an asterisk are close to weekends with a home game for the [https://uwbadgers.com/sports/football/schedule UW Badgers football team]. Hotel availability around these dates is often limited if booked on short notice.]''
==Abstract==
<div id="Di">
'''Zichao (Wendy) Di (Argonne National Laboratory)'''


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


<div id="Chandler">
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.
====Thomas G. J. Chandler (UW)====
Title: Fluid-structure interactions in active complex fluids


Fluid anisotropy is central to many biological systems, from rod-like bacteria that self-assemble into dense swarms that function as fluids, to the cell cytoskeleton where the active alignment of stiff biofilaments is crucial to cell division. Nematic liquid crystals provide a powerful model for studying these complex environments. However, large immersed bodies elastically frustrate these fluids, leading to intricate interactions. This frustration can be alleviated through body deformations, at the cost of introducing internal stresses. Additionally, active stresses, arising from particle motility or molecular activity, disrupt nematic order by driving flows. In this presentation, I will demonstrate how complex variables enable analytical solutions to a broad range of problems, offering key insights into the roles of body geometry, anchoring conditions, interaction dynamics, activity-induced flows, and body deformations in many biological settings.
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.


<div id="Fraser">
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.
====Adrian Fraser (Colorado)====
Title: Destabilization of transverse waves by periodic shear flows


Periodic shear flows have the peculiar property that they are unstable to large-scale, transverse perturbations, and that this instability proceeds via a negative-eddy-viscosity mechanism (Dubrulle & Frisch, 1991). In this talk, I will show an example where this property causes transverse waves to become linearly unstable: a sinusoidal shear flow in the presence of a uniform, streamwise magnetic field in the framework of incompressible MHD. This flow is unstable to a KH-like instability for sufficiently weak magnetic fields, and uniform magnetic fields permit transverse waves known as Alfvén waves. Under the right conditions, these Alfvén waves become unstable, presenting a separate branch of instability that persists for arbitrarily strong magnetic fields which otherwise suppress the KH-like instability. After characterizing these waves with the help of a simple asymptotic expansion, I will show that they drive soliton-like waves in nonlinear simulations. With time permitting, I will discuss other fluid systems where similar dynamics are or may be found, including stratified flows and plasma drift waves.
<div id="Behnoudfar">
'''Pouria Behnoudfar (UW Madison)'''


<div id="Cockburn">
Title: Bridging Conceptual and Operational Models: An Explainable AI Framework for Next-Generation Climate Emulators
====Bernardo Cockburn (Minnesota)====
Title: Transforming stabilization into spaces


In the framework of finite element methods for ordinary differential equations, we consider the continuous Galerkin method (introduced in 72) and the discontinuous Galerkin method (introduced in 73/74). We uncover the fact that both methods discretize the time derivative in exactly the same form, and discuss a few of its consequences. We end by briefly describing our ongoing work on the extension of this result to some Galerkin methods for partial differential equations.
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 ==
== Archived semesters ==


*[[Applied/ACMS/Spring2025|Spring 2025]]
*[[Applied/ACMS/Fall2024|Fall 2024]]
*[[Applied/ACMS/Fall2024|Fall 2024]]
*[[Applied/ACMS/Spring2024|Spring 2024]]
*[[Applied/ACMS/Spring2024|Spring 2024]]

Latest revision as of 19:47, 24 September 2025


Applied and Computational Mathematics Seminar


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


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