Applied/ACMS: Difference between revisions

From UW-Math Wiki
Jump to navigation Jump to search
VisualWikitext
 
(948 intermediate revisions by 23 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:''' See [https://lists.math.wisc.edu/listinfo/acms mailing list] website.
*'''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 2014 Semester ==


== '''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)
|-
|-
|Jan 17
|Sep 19*
|[http://www.math.dartmouth.edu/~gillmana/ Adrianna Gillman] (Dartmouth)
|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory)
|''[[Applied/ACMS/absS14#Adrianna Gillman (Dartmouth)|Fast direct solvers for linear partial differential equations]]''
|[[#Di|Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science]]
|Jean-Luc (job talk)
|Rycroft/Li
|-
|Sep 26
|[https://scholar.google.com/citations?user=Imuw5CMAAAAJ&hl=en&oi=ao Pouria Behnoudfar] (UW)
|[[#Behnoudfar|Bridging Conceptual and Operational Models: A Physics-Guided Machine Learning Framework for Enhanced Climate Simulation]]
|Spagnolie
|-
|Oct 3
|
|
|
|-
|-
|Jan 24, '''4 pm, B239'''
|Oct 10*
|[http://www.yanivplan.com Yaniv Plan] (Michigan)
|[https://www.alexandriavolkening.com Alexandria Volkening] (Purdue)
|''[[Applied/ACMS/absS14#Yaniv Plan (Michigan)|Low-dimensionality in mathematical signal processing]]''
|TBD
|Jean-Luc (job talk)
|Rycroft
|-
|-
|Feb 7
|Oct 17*
|[http://scholar.google.com/scholar?hl=en&as_sdt=0,50&q=harvey+segur Harvey Segur] (Colorado)
|[https://www.nickderr.me/ Nick Derr] (UW)
|''[[Applied/ACMS/absS14#Harvey Segur (Colorado)|The nonlinear Schrödinger equation, dissipation and ocean swell]]''
|TBD
|Saverio
|Spagnolie
|-
|-
|Feb 14
|Oct 24
|[https://engineering.purdue.edu/ChE/People/ptProfile?id=11307 Sangtae Kim] (Purdue)
|[https://cims.nyu.edu/~oneil/ Mike O'Neil] (Courant)
|''[[Applied/ACMS/absS14#Sangtae Kim (Purdue)|The Faxén Laws of Stokes flow and their connection to singularity solutions]]''
|TBD
|Saverio
|Spagnolie
|-
|-
|Feb 21
|Oct 31
|[http://www.astro.wisc.edu/our-people/faculty/donghia-elena/ Elena D'Onghia] (UW)
|[https://people.math.wisc.edu/~hhong78/ Hyukpyo Hong] (UW)
|''[[Applied/ACMS/absS14#Elena D'Onghia (UW)|The origin of spiral arms in galactic disks]]''
|TBD
|Saverio
|Spagnolie
|-
|-
|'''Feb 28, 4pm, B239'''
|Nov 7*
|[http://math.nyu.edu/faculty/shelley/ Michael Shelley] (Courant)
|[https://thales.mit.edu/bush/ John Bush] (MIT)
|''[[Applied/ACMS/absS14#Michael Shelley (Courant)|Mathematical models of soft active materials]]''
|TBD
|'''Colloquium'''
|Spagnolie
|-
|-
|Mar 7
|Nov 14
|[http://math.mit.edu/~hand/ Paul Hand] (MIT)
|[https://sites.google.com/andrew.cmu.edu/yukunyue/home Yukun Yue] (UW)
|''[[Applied/ACMS/absS14#Paul Hand (MIT)|Evaluating signal recovery algorithms with semirandom models]]''
|TBD
|Jean-Luc, Saverio
|Spagnolie
|-
|-
|Mar 14
|Nov 21*
|[http://www.math.wisc.edu/~shottovy/ Scott Hottovy] (UW)
|[https://jesnial.github.io/ Jessie Levillain] (CNES/INSA Toulouse)
|''[[Applied/ACMS/absS14#Scott Hottovy (UW)|Modeling with stochastic differential equations: A noisy circuit and thresholds for rainfall]]''
|TBD
|Saverio
|Ohm
|-
|-
|Mar 21
|Nov 28
|Spring recess
|Thanksgiving
|
|
|
|
|-
|-
|'''Mar 26, 7pm'''
|Dec 5
|[https://www.dpmms.cam.ac.uk/people/t.tokieda/ Tadashi Tokieda] (Cambridge)
|[https://mesomod.weebly.com/ Jiamian Hu] (UW)
|''[[Applied/ACMS/absS14#Tadashi Tokieda (Cambridge)|TBA]]''
|TBD
|Jean-Luc, Saverio
|Chen
|-
|Mar 28
|[http://www.math.canterbury.ac.nz/~m.steel/ Mike Steel] (University of Canterbury)
|''[[Applied/ACMS/absS14#Mike Steel|Tractable models for some discrete random processes arising in evolutionary biology]]''
|Sebastien
|-
|Apr 4
|[https://directory.engr.wisc.edu/ie/faculty/mclay_laura/ Laura McLay] (UW)
|''[[Applied/ACMS/absS14#Laura McLay (UW)|TBA]]''
|Julie
|-
|Apr 11
|[http://www.math.wisc.edu/~sqchen/ Shengqian "Chessy" Chen] (UW)
|''[[Applied/ACMS/absS14#Shengqian Chen (UW)|TBA]]''
|Saverio
|-
|Apr 18
|[http://www.nwra.com/resumes/lelong/ Marie-Pascale Lelong] (NorthWest Research Associates)
|''[[Applied/ACMS/absS14#Marie Pascale Lelong|TBA]]''
|Leslie
|-
|Apr 25
|[http://math.berkeley.edu/~wilken/ Jon Wilkening] (UC Berkeley)
|''[[Applied/ACMS/absS14#Jon Wilkening (UC_Berkeley)|TBA]]''
|Saverio
|-
|-
|May 2
|Dec 12
|[http://www.math.utah.edu/~choheneg/ Christel Hohenegger] (Utah)
|[https://sites.google.com/a/brandeis.edu/tfai/home Thomas Fai] (Brandeis)
|''[[Applied/ACMS/absS14#Christel Hohenegger (Utah)|TBA]]''
|TBD
|Saverio
|Rycroft
|-
|May 9
|[http://amath.colorado.edu/faculty/jdm/index.html Jim Meiss] (Colorado)  
|''[[Applied/ACMS/absS14#Jim_Meiss_(Colorado)|TBA]]''
|Saverio
|}
|}
''[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
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.


<br>
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: A Physics-Guided Machine Learning Framework for Enhanced Climate Simulation
 
Operational models are high-resolution and contain many crucial variables. Despite numerous successes, biases persist in most of these models, particularly in identifying extreme events and accurately reproducing statistics. However, it is challenging to directly modify these models to improve their performance. Conceptual and intermediate coupled models accurately characterize certain features of nature. Yet, these coarse-grained models contain only a subset of variables within a specific domain. By leveraging the strengths of models of varying complexities, we develop a robust physics-driven machine learning modeling framework as the next-generation climate emulator, which bridges the model hierarchy through effective latent space data assimilation. The resulting bridging model not only inherits the benefits of the operational models, including their high resolution and comprehensive set of variables, but also globally enhances the accuracy through local dynamical and statistical improvements provided by the simpler models.  Our developed latent space technique identifies the dominant nonlinear features, which facilitates effective communication between models. The machine learning representation of the bridging model significantly enhances simulation efficiency, providing high-quality datasets and advancing the quantification of uncertainty in extreme events. The framework is applied to improve the performance of CMIP6 simulations in characterizing ENSO complexity by exploiting simpler yet statistically accurate 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/Fall2019|Fall 2019]]
*[[Applied/ACMS/Spring2019|Spring 2019]]
*[[Applied/ACMS/Fall2018|Fall 2018]]
*[[Applied/ACMS/Spring2018|Spring 2018]]
*[[Applied/ACMS/Fall2017|Fall 2017]]
*[[Applied/ACMS/Spring2017|Spring 2017]]
*[[Applied/ACMS/Fall2016|Fall 2016]]
*[[Applied/ACMS/Spring2016|Spring 2016]]
*[[Applied/ACMS/Fall2015|Fall 2015]]
*[[Applied/ACMS/Spring2015|Spring 2015]]
*[[Applied/ACMS/Fall2014|Fall 2014]]
*[[Applied/ACMS/Spring2014|Spring 2014]]
*[[Applied/ACMS/Fall2013|Fall 2013]]
*[[Applied/ACMS/Fall2013|Fall 2013]]
*[[Applied/ACMS/Spring2013|Spring 2013]]
*[[Applied/ACMS/Spring2013|Spring 2013]]

Latest revision as of 03:51, 9 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: A Physics-Guided Machine Learning Framework for Enhanced Climate Simulation 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: A Physics-Guided Machine Learning Framework for Enhanced Climate Simulation

Operational models are high-resolution and contain many crucial variables. Despite numerous successes, biases persist in most of these models, particularly in identifying extreme events and accurately reproducing statistics. However, it is challenging to directly modify these models to improve their performance. Conceptual and intermediate coupled models accurately characterize certain features of nature. Yet, these coarse-grained models contain only a subset of variables within a specific domain. By leveraging the strengths of models of varying complexities, we develop a robust physics-driven machine learning modeling framework as the next-generation climate emulator, which bridges the model hierarchy through effective latent space data assimilation. The resulting bridging model not only inherits the benefits of the operational models, including their high resolution and comprehensive set of variables, but also globally enhances the accuracy through local dynamical and statistical improvements provided by the simpler models.  Our developed latent space technique identifies the dominant nonlinear features, which facilitates effective communication between models. The machine learning representation of the bridging model significantly enhances simulation efficiency, providing high-quality datasets and advancing the quantification of uncertainty in extreme events. The framework is applied to improve the performance of CMIP6 simulations in characterizing ENSO complexity by exploiting simpler yet statistically accurate models. 

Archived semesters


Return to the Applied Mathematics Group Page

Retrieved from "https://wiki.math.wisc.edu/index.php?title=Applied/ACMS&oldid=29023"