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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:''' [http://www.math.wisc.edu/~qinli/ Qin Li], [http://www.math.wisc.edu/~spagnolie/ Saverio Spagnolie] and [http://www.math.wisc.edu/~jeanluc Jean-Luc Thiffeault]
*'''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+join@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>  


 
== '''Fall 2025''' ==
== Fall 2020  ==
 
{| cellpadding="8"
{| cellpadding="8"
!align="left" | date
! align="left" |Date
!align="left" | speaker
! align="left" |Speaker
!align="left" | title
! align="left" |Title
!align="left" | host(s)
! align="left" |Host(s)
|-
|-
| Sep 11
|Sep 19*
|[https://cee.stanford.edu/people/nicholas-ouellette Nick Ouellette (Stanford)]
|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory)
|''[[Applied/ACMS/absF20#Nick Ouellette (Stanford)|Tensor Geometry in the Turbulent Cascade]]''
|[[#Di|Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science]]
|Jean-Luc
|Rycroft/Li
|-
|-
| Sep 18
|Sep 26
|[https://www.researchgate.net/profile/Harry_Lee24 Harry Lee (UW-Madison and UMich)]
|[https://scholar.google.com/citations?user=Imuw5CMAAAAJ&hl=en&oi=ao Pouria Behnoudfar] (UW)
|''[[Applied/ACMS/absF20#Harry Lee (UW-Madison, UMich)|Recent extension of V.I. Arnold's and J.L. Synge's mathematical theory of shear flows]]''
|[[#Behnoudfar|Bridging Conceptual and Operational Models: An Explainable AI Framework for Next-Generation Climate Emulators]]
|Wally
|Spagnolie
|-
|-
| Sep 25
|Oct 3
|[https://www.mtholyoke.edu/people/spencer-smith Spencer Smith (Mount Holyoke)]
|
|''[[Applied/ACMS/absF20#Spencer Smith (Mount Holyoke)|Braids on a lattice and maximally efficient mixing in active matter systems]]''
|Jean-Luc
|-
| Oct 2
|[https://zhizhenz.ece.illinois.edu/ Zhizhen Jane Zhao] (UIUC)
|''[[Applied/ACMS/absF20#Zhizhen Jane Zhao (UIUC)|Exploiting Group and Geometric Structures for Massive Data Analysis]]''
| Li & Chen
|
|
|
|
|-
|-
| Oct 9
|Oct 10*
|[https://igppweb.ucsd.edu/~mmorzfeld/ Matthias Morzfeld] (Scripps & UCSD)
|[https://www.alexandriavolkening.com Alexandria Volkening] (Purdue)
|''[[Applied/ACMS/absF20#Matthias Morzfeld (Scripps & UCSD)|What is Bayesian inference, why is it useful in Earth science and why is it challenging to do numerically?]]''
|TBD
| Chen
|Rycroft
|
|
|-
|-
| Oct 16
|Oct 17*
|[https://jingweihu-math.github.io/webpage/ Jingwei Hu] (Purdue)
|[https://www.nickderr.me/ Nick Derr] (UW)
|''[[Applied/ACMS/absF20#Jingwei Hu (Purdue)|A new stability and convergence proof of the Fourier-Galerkin spectral method for the spatially homogeneous Boltzmann equation]]''
|TBD
| Li
|Spagnolie
|
|-
|-
| Oct 23
|Oct 24
|[https://www.aos.wisc.edu/~dvimont/Home.html Dan Vimont] (UW-Madison, AOS)
|[https://cims.nyu.edu/~oneil/ Mike O'Neil] (Courant)
|''[[Applied/ACMS/absF20#Dan Vimont (UW-Madison, AOS)|Advances in Linear Inverse Modeling for Understanding Tropical Pacific Climate Variability]]''
|TBD
| Stechmann
|Spagnolie
|
|-
|-
| Oct 30
|Oct 31
|[http://www.dam.brown.edu/people/spsmith/ Sam Punshon-Smith] (Brown)
|[https://people.math.wisc.edu/~hhong78/ Hyukpyo Hong] (UW)
|''[[Applied/ACMS/absF20#Sam Punshon-Smith (Brown)|TBD]]''
|TBD
| Li
|Spagnolie
|
|-
|-
| Nov 6
|Nov 7*
|[https://www.math.uci.edu/people/yimin-zhong Yimin Zhong] (UCI, Duke)
|[https://thales.mit.edu/bush/ John Bush] (MIT)
|''[[Applied/ACMS/absF20#Yimin Zhong (UCI and Duke)|TBD]]''
|TBD
|Li
|Spagnolie
|
|-
|-
| Nov 13
|Nov 14
|[https://www.cmu.edu/biolphys/deserno/ Markus Deserno] (CMU)
|[https://sites.google.com/andrew.cmu.edu/yukunyue/home Yukun Yue] (UW)
|''[[Applied/ACMS/absF20#Markus Deserno (CMU)|Spontaneous curvature, differential stress, and bending modulus of asymmetric lipid membranes]]''
|TBD
|Spagnolie
|Spagnolie
|-
|-
| Nov 20
|Nov 21*
|[https://www.usna.edu/Users/math/lunasin/index.php/ Evelyn Lunasin] (USNA)
|[https://jesnial.github.io/ Jessie Levillain] (CNES/INSA Toulouse)
|''[[Applied/ACMS/absF20#Evelyn Lunasin (USNA)|TBD]]''
|TBD
|Jean-Luc & Chen
|Ohm
|-
|-
| Nov 27
|Nov 28
|''Thangksgiving recess''
|Thanksgiving
|
|
|
|
|-
|-
| Dec 4
|Dec 5
||[https://www.math.arizona.edu/people/chertkov Michael Chertkov] (U. Arizona)
|[https://mesomod.weebly.com/ Jiamian Hu] (UW)
|''[[Applied/ACMS/absF20#Michael Chertkov (U Arizona)|TBD]]''
|TBD
|Zepeda-Nunez
|Chen
|
|
|-
|-
|Dec 12
|[https://sites.google.com/a/brandeis.edu/tfai/home Thomas Fai] (Brandeis)
|TBD
|Rycroft
|}
|}
''[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.]''


== Future semesters ==
==Abstract==


*[[Applied/ACMS/Spring2021|Spring 2021]]
<div id="Di">
'''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.
 
<div id="Behnoudfar">
'''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 ==
== Archived semesters ==


*[[Applied/ACMS/Spring2025|Spring 2025]]
*[[Applied/ACMS/Fall2024|Fall 2024]]
*[[Applied/ACMS/Spring2024|Spring 2024]]
*[[Applied/ACMS/Fall2023|Fall 2023]]
*[[Applied/ACMS/Spring2023|Spring 2023]]
*[[Applied/ACMS/Fall2022|Fall 2022]]
*[[Applied/ACMS/Spring2022|Spring 2022]]
*[[Applied/ACMS/Fall2021|Fall 2021]]
*[[Applied/ACMS/Spring2021|Spring 2021]]
*[[Applied/ACMS/Fall2020|Fall 2020]]
*[[Applied/ACMS/Spring2020|Spring 2020]]
*[[Applied/ACMS/Spring2020|Spring 2020]]
*[[Applied/ACMS/Fall2019|Fall 2019]]
*[[Applied/ACMS/Fall2019|Fall 2019]]

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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