Applied/ACMS: Difference between revisions

From UW-Math Wiki
Jump to navigation Jump to search
← Older edit
VisualWikitext
Qinli (talk | contribs)
365 edits
Nchen29 (talk | contribs)
11 edits
 
(342 intermediate revisions by 16 users not shown)
Line 5: Line 5:
*'''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/~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>  


== Spring 2022  ==
== '''Fall 2025''' ==
 
{| 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)
|-
|-
| Feb 11
|Sep 19*
|[https://notbohm.ep.wisc.edu/ Jacob Notbohm] (UW)
|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory)
|''[[Applied/ACMS/absS22#Jacob Notbohm (UW)|Collective Cell Migration: Rigidity Transition and the Eyes of the Cell]]''
|[[#Di|Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science]]
|Spagnolie
|Rycroft/Li
|-
|-
| Feb 18
|Sep 26
|[https://pi.math.cornell.edu/~ajt/ Alex Townsend] (Cornell)
|[https://scholar.google.com/citations?user=Imuw5CMAAAAJ&hl=en&oi=ao Pouria Behnoudfar] (UW)
|''[[Applied/ACMS/absS22#Alex Townsend (Cornell)|What networks of oscillators spontaneously synchronize?]]''
|[[#Behnoudfar|Bridging Conceptual and Operational Models: An Explainable AI Framework for Next-Generation Climate Emulators]]
|Li
|-
| Feb 25
|[https://dedalus-project.org/ Geoffrey Vasil] (Sydney)
|''[[Applied/ACMS/absS22#Geoffrey Vasil (Sydney)|The mechanics of a large pendulum chain]]''
|Spagnolie
|Spagnolie
|-
|-
| Mar 4
|Oct 3
|[https://www.math.purdue.edu/~zhan1966/ Xiangxiong Zhang] (Purdue)
|
|''[[Applied/ACMS/absS22#Xiangxiong Zhang (Purdue)|Recent Progress on Q^k Spectral Element Method: Accuracy, Monotonicity and Applications]]''
|Stechmann
|-
| Mar 11
|[https://www.microsoft.com/en-us/research/people/penzhan/ Pengchuan Zhang] (Senior researcher, Microsoft)
|''[[Applied/ACMS/absS22#Pengchuan Zhang (Microsoft)|Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference]]''
|Li
|-
| Mar 18
|Spring break
|
|
|
|
|-
|-
| Mar 25
|Oct 10*
|[https://cfwebprod.sandia.gov/cfdocs/CompResearch/templates/insert/profile.cfm?mataylo Mark Taylor] (Sandia)
|[https://www.alexandriavolkening.com Alexandria Volkening] (Purdue)
|''[[Applied/ACMS/absS22#Mark Taylor (Sandia)|An overview of the numerical methods in the atmospheric component of the Energy Exascale Earth System Model]]''
|TBD
|Smith, Stechmann
|Rycroft
|-
|-
| Apr 1
|Oct 17*
|[https://www.swojtowytsch.com/ Stephan Wojtowytsch] (TAMU)
|[https://www.nickderr.me/ Nick Derr] (UW)
|''[[Applied/ACMS/absS22#Stephan Wojtowytsch (TAMU)|Neural network approximation in shallow and deep learning]]''
|TBD
|Li
|Spagnolie
|-
|-
| Apr 8
|Oct 24
|[https://web.ma.utexas.edu/users/rhjin/ Ruhui Jin] (UT-Austin)
|[https://cims.nyu.edu/~oneil/ Mike O'Neil] (Courant)
|''[[Applied/ACMS/absS22#Ruhui Jin (UT-Austin)|TBA]]''
|TBD
|Li
|Spagnolie
|-
|-
| Apr 15
|Oct 31
|[https://pages.cs.wisc.edu/~sifakis/ Eftychios Sifakis] (UW)
|[https://people.math.wisc.edu/~hhong78/ Hyukpyo Hong] (UW)
|''[[Applied/ACMS/absS22#Eftychios Sifakis (UW)|TBA]]''
|TBD
|Spagnolie
|Spagnolie
|-
|-
| Apr 22
|Nov 7*
|[https://sites.uwm.edu/hinow/research/ Peter Hinow] (UWM)
|[https://thales.mit.edu/bush/ John Bush] (MIT)
|''[[Applied/ACMS/absS22#Peter Hinow (UWM)|Tiny Giants - Mathematics Looks at Zooplankton]]''
|TBD
|Spagnolie
|Spagnolie
|-
|-
| Apr 29
|Nov 14
|[https://vivo.brown.edu/display/tpowers Thomas Powers] (Brown)
|[https://sites.google.com/andrew.cmu.edu/yukunyue/home Yukun Yue] (UW)
|''[[Applied/ACMS/absS22#Thomas Powers (Brown)|TBA]]''
|TBD
|Spagnolie
|Spagnolie
|-
|Nov 21*
|[https://jesnial.github.io/ Jessie Levillain] (CNES/INSA Toulouse)
|TBD
|Ohm
|-
|Nov 28
|Thanksgiving
|
|
|-
|Dec 5
|[https://mesomod.weebly.com/ Jiamian Hu] (UW)
|TBD
|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.]''
==Abstract==
<div id="Di">
'''Zichao (Wendy) Di (Argonne National Laboratory)'''
Title: Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science


== Future semesters ==
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.


*[[Applied/ACMS/Fall2022|Fall 2022]]
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/Fall2021|Fall 2021]]
*[[Applied/ACMS/Spring2021|Spring 2021]]
*[[Applied/ACMS/Spring2021|Spring 2021]]

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


Return to the Applied Mathematics Group Page

Retrieved from "https://wiki.math.wisc.edu/index.php?title=Applied/ACMS&oldid=29184"