Applied/ACMS: Difference between revisions

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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+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 2024  ==
== '''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 2
|[https://people.math.wisc.edu/~chr/ Chris Rycroft] (UW)
|''The reference map technique for simulating complex materials and multi-body interactions''
* '''Zoom:''' https://uwmadison.zoom.us/j/92068711076?pwd=dnQvOWFRb0xIRWFXaU5CWnVia0VOdz09
* '''Passcode:''' 847281
|
|-
| Feb 9
|[https://users.flatironinstitute.org/~sweady/ Scott Weady] (Flatiron Institute)
|''Entropy methods in active suspensions''
|Saverio and Laurel
|-
|-
| Feb 16
|Sep 19*
|[http://stokeslet.ucsd.edu/ David Saintillan] (UC San Diego)
|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory)
|''[[Applied/ACMS/absS24#David Saintillan (UC San Diego)|TBA]]''
|Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science
|Saverio and Tom
|Rycroft/Li
|-
|-
| Feb 23
|Sep 26
|[https://cersonsky-lab.github.io/website/ Rose Cersonsky] (UW)
|[https://scholar.google.com/citations?user=Imuw5CMAAAAJ&hl=en&oi=ao Pouria Behnoudfar] (UW)
|''[[Applied/ACMS/absS24#Rose Cersonsky (UW)|TBA]]''
|TBD
|Chris
|Spagnolie
|-
|-
| Mar 1 [4:00pm Colloquium]
|Oct 3
|[https://users.oden.utexas.edu/~pgm/ Per-Gunnar Martinsson] (UT Austin)
|''[[Applied/ACMS/absS24#Per-Gunnar Martinsson (UT-Austin)|TBA]]''
|Li
|-
| Mar 8
|
|
|
|
|
|
|-
|-
| Mar 15
|Oct 10*
|[https://www.math.purdue.edu/~qi117/personal.html/ Di Qi] (Purdue University)
|[https://www.alexandriavolkening.com Alexandria Volkening] (Purdue)
|''[[Applied/ACMS/absS24#Di Qi (Purdue University)|TBA]]''
|TBD
|Chen
|Rycroft
|-
|-
| Mar 22
|Oct 17*
|
|[https://www.nickderr.me/ Nick Derr] (UW)
|
|TBD
|
|Spagnolie
|-
|-
| Mar 29
|Oct 24
|Spring break
|[https://cims.nyu.edu/~oneil/ Mike O'Neil] (Courant)
|
|TBD
|
|Spagnolie
|-
|-
| Apr 5
|Oct 31
|[https://www.jinlongwu.org/ Jinlong Wu] (UW)
|[https://people.math.wisc.edu/~hhong78/ Hyukpyo Hong] (UW)
|''[[Applied/ACMS/absS24#Jinlong Wu (UW)|TBA]]''
|TBD
|Saverio
|Spagnolie
|-
|-
| Apr 12
|Nov 7*
|[https://zayascaban.labs.wisc.edu/ Gabriel Zayas-Caban] (UW)
|[https://thales.mit.edu/bush/ John Bush] (MIT)
|''[[Applied/ACMS/absS24#Gabriel Zayas-Caban (UW)|TBA]]''
|TBD
|Li
|Spagnolie
|-
|-
| Apr 19
|Nov 14
|[https://www.nist.gov/people/anthony-j-kearsley Tony Kearsley] (NIST)
|[https://sites.google.com/andrew.cmu.edu/yukunyue/home Yukun Yue] (UW)
|''[[Applied/ACMS/absS24#Tony Kearsley (NIST)|TBA]]''
|TBD
|Fabien
|Spagnolie
|-
|-
| Apr 26
|Nov 21*
|[https://math.oregonstate.edu/directory/malgorzata-peszynska Malgorzata Peszynska] (Oregon State)
|[https://jesnial.github.io/ Jessie Levillain] (CNES/INSA Toulouse)
|''[[Applied/ACMS/absS24#Malgorzata Peszynska (Oregon State)|TBA]]''
|TBD
|Fabien
|Ohm
|-
|-
|Nov 28
|Thanksgiving
|
|
|
|
|-
|Dec 5
|[https://mesomod.weebly.com/ Jiamian Hu] (UW; Engineering)
|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.]''


== Abstracts ==
==Abstract==


==== Chris Rycroft (UW–Madison) ====
<div id="Chandler">
Title: The reference map technique for simulating complex materials and multi-body interactions
'''Zichao (Wendy) Di (Argonne National Laboratory)'''


Conventional computational methods often create a dilemma for fluid–structure interaction problems. Typically, solids are simulated using a Lagrangian approach with grid that moves with the material, whereas fluids are simulated using an Eulerian approach with a fixed spatial grid, requiring some type of interfacial coupling between the two different perspectives. Here, a fully Eulerian method for simulating structures immersed in a fluid will be presented [1]. By introducing a reference map variable to model finite-deformation constitutive relations in the structures on the same grid as the fluid, the interfacial coupling problem is highly simplified. The method is particularly well suited for simulating soft, highly-deformable materials and many-body contact problems [2], and several examples in two and three dimensions [3] will be presented.
Title: Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science


# K. Kamrin, C. H. Rycroft, and J.-C. Nave, J. Mech. Phys. Solids '''60''', 1952–1969 (2012). [https://doi.org/10.1016/j.jmps.2012.06.003 <nowiki>[DOI link]</nowiki>]
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.
# C. H. Rycroft ''et al.'', J. Fluid Mech. '''898''', A9 (2020). [https://doi.org/10.1017/jfm.2020.353 <nowiki>[DOI link]</nowiki>]
# Y. L. Lin, N. J. Derr, and C. H. Rycroft, Proc. Natl. Acad. Sci. '''119''', e2105338118 (2022). [https://doi.org/10.1073/pnas.2105338118 <nowiki>[DOI link]</nowiki>]


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.


==== Scott Weady (Flatiron Institute) ====
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="Fraser"><div id="Luedtke"><div id="Zhdankin"><div id="Boffi"><div id="Shankar"><div id="Loevbak">
 
<div id="Lu"><div id="Vogman"><div id="Cockburn">
Title: Entropy methods in active suspensions
== Archived semesters ==
 
Collections of active particles, such as suspensions of E. coli or mixtures of microtubules and molecular motors, can exhibit rich non-equilibrium dynamics due to a combination of activity, hydrodynamic interactions, and steric stresses. Continuum kinetic theories, which characterize the set of particle configurations through a continuous distribution function, provide a powerful framework for analyzing such systems and connecting their micro- to macroscopic dynamics. The probabilistic formulation of kinetic theories leads naturally to a characterization in terms of entropy, whether thermodynamic or information-theoretic. In equilibrium systems, entropy strictly increases and always tends towards steady state. This no longer holds in active systems, however entropy still has a convenient mathematical structure. In this talk, we use entropy methods, specifically variational principles involving the relative entropy functional, to study the nonlinear dynamics and stability of active suspensions in the context of the Doi-Saintillan-Shelley kinetic theory. We first present a class of moment closures that arise as constrained minimizers of the relative entropy, and show these closures preserve the kinetic theory's stability and entropic structure while admitting efficient numerical simulation. We then derive variational bounds on relative entropy fluctuations for apolar active suspensions that are closely related to the moment closures. These bounds provide conditions for global stability and yield estimates of time-averaged order parameters. Finally, we discuss applications of these methods to polar active suspensions.
 
== Future semesters ==


*[[Applied/ACMS/Spring2025|Spring 2025]]
*[[Applied/ACMS/Fall2024|Fall 2024]]
*[[Applied/ACMS/Fall2024|Fall 2024]]
 
*[[Applied/ACMS/Spring2024|Spring 2024]]
== Archived semesters ==
 
*[[Applied/ACMS/Fall2023|Fall 2023]]
*[[Applied/ACMS/Fall2023|Fall 2023]]
*[[Applied/ACMS/Spring2023|Spring 2023]]
*[[Applied/ACMS/Spring2023|Spring 2023]]

Latest revision as of 02:58, 5 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) TBD 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; Engineering) 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.
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