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*'''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://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],  
*'''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  ==
== '''Spring 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
|Jan 31
|[https://people.math.wisc.edu/~chr/ Chris Rycroft] (UW)
|[https://people.math.wisc.edu/~tgchandler/ Thomas Chandler] (UW)
|''The reference map technique for simulating complex materials and multi-body interactions''
|[[#Chandler|''Fluid–structure interactions in active complex fluids'']]
|
|Spagnolie
|-
|-
| Feb 9
|Feb 7
|[https://users.flatironinstitute.org/~sweady/ Scott Weady] (Flatiron Institute)
|[https://afraser3.github.io/ Adrian Fraser] (Colorado)
|''Entropy methods in active suspensions''
|[[#Fraser|''Destabilization of transverse waves by periodic shear flows'']]
|Saverio and Laurel
|Spagnolie
|-
|-
| Feb 16
|Feb 14
|[http://stokeslet.ucsd.edu/ David Saintillan] (UC San Diego)
|[https://jrluedtke.github.io/ Jim Luedtke] (UW)
|''Hydrodynamics of active nematic surfaces''
|[[#Luedtke|Using integer programming for verification of binarized neural networks]]
|Saverio and Tom
|Spagnolie
|-
|-
| Feb 23
|Feb 21
|[https://cersonsky-lab.github.io/website/ Rose Cersonsky] (UW)
|[https://zhdankin.physics.wisc.edu/ Vladimir Zhdankin] (UW)
|''Data-driven approaches to chemical and materials sciences''
|[[#Zhdankin|Exploring astrophysical plasma turbulence with particle-in-cell methods]]
|Chris
|Spagnolie
|-
|-
| Mar 1 [4:00pm Colloquium]
|Feb 28
|[https://users.oden.utexas.edu/~pgm/ Per-Gunnar Martinsson] (UT Austin)
|[https://nmboffi.github.io/ Nick Boffi] (CMU)
|''[[Applied/ACMS/absS24#Per-Gunnar Martinsson (UT-Austin)|TBA]]''
|[[#Boffi|TBA]]
|Li
|Li
|-
|-
| Mar 8
|Mar 7
|[https://www.physics.wisc.edu/directory/jorge-rogerio/ Rogerio Jorge] (UW-Madison)
|[https://sites.lsa.umich.edu/shankar-lab/ Suraj Shankar] (Michigan)
|''The Direct Optimization Framework in Stellarator Design: Transport and Turbulence Optimization''
|[[#Shankar|TBA]]
|Spagnolie
|-
|Mar 14
|[https://lu.seas.harvard.edu/ Yue Lu] (Harvard) '''[Colloquium]'''
|[[#Lu|TBA]]
|Li
|Li
|-
|-
| Mar 15
|Mar 21
|[https://www.math.purdue.edu/~qi117/personal.html/ Di Qi] (Purdue University)
|[https://people.llnl.gov/vogman1 Genia Vogman] (LLNL)
|''Statistical Reduced-Order Models and Random Batch Method for Complex Multiscale Systems''
|[[#Vogman|TBA]]
|Chen
|Li
|-
|-
| Mar 22
|Mar 28
|
|''Spring Break''
|
|
|
|
|-
|-
| Mar 29
|Apr 4
|Spring break
|TBA
|
|
|
|
|-
|-
| Apr 5
|Apr 11
|[https://www.jinlongwu.org/ Jinlong Wu] (UW)
|[https://meche.mit.edu/people/faculty/pierrel@mit.edu Pierre Lermusiaux] (MIT)
|''Operator learning for data-driven closure models of complex dynamical systems''
|[[#Lermusiaux|TBA]]
|Saverio
|Chen
|-
|-
| Apr 12
|Apr 18
|[https://zayascaban.labs.wisc.edu/ Gabriel Zayas-Caban] (UW)
|[https://www.math.uci.edu/~jxin/ Jack Xin] (UC Irvine) '''[Colloquium]'''
|''Unveiling Bias in Sequential Decision Making: A Causal Inference Approach for Stochastic Service Systems''
|[[#Xin|TBA]]
|Li
|
|-
| Apr 19
|[https://www.nist.gov/people/anthony-j-kearsley Tony Kearsley] (NIST)
|Control of inward solidification in Cryobiology
|Fabien
|-
|-
| Apr 26
|Apr 25
|[https://math.oregonstate.edu/directory/malgorzata-peszynska Malgorzata Peszynska] (Oregon State)
|[https://www-users.cse.umn.edu/~bcockbur/ Bernardo Cockburn] (Minnesota)
|Multiphysics across the scales for applications in permafrost
|[[#Cockburn|''Transforming stabilization into spaces'']]
|Fabien
| Stechmann, Fabien
|-
|-
|
|May 2
|
|[https://sylviaherbert.com/ Sylvia Herbert] (UCSD)
|[[#Herbert|TBA]]
|Chen
|}
|}


== Abstracts ==
==Abstracts==


==== Chris Rycroft (UW–Madison) ====
<div id="Chandler">
Title: The reference map technique for simulating complex materials and multi-body interactions
====Thomas G. J. Chandler (UW)====
Title: Fluid-structure interactions in active complex fluids


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.
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.


# 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>]
<div id="Fraser">
# C. H. Rycroft ''et al.'', J. Fluid Mech. '''898''', A9 (2020). [https://doi.org/10.1017/jfm.2020.353 <nowiki>[DOI link]</nowiki>]
====Adrian Fraser (Colorado)====
# 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>]
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.


==== Scott Weady (Flatiron Institute) ====
<div id="Luedtke">
====Jim Luedtke (UW)====
Title: Using integer programming for verification of binarized neural networks


Title: Entropy methods in active suspensions
Binarized neural networks (BNNs) are neural networks in which the weights are binary and the activation functions are the sign function. Verification of BNNs against input perturbation is one way to measure robustness of BNNs. BNN verification can be formulated as an integer linear optimization problem and hence can in theory be solved by state-of-the art methods for integer programming such as the branch-and-cut algorithm implemented in solvers like Gurobi. Unfortunately, the natural formulation is often difficult to solve in practice, even by the best such solvers, due to large integrality gap induced by its so-called "big-M" constraints. We present simple but effective techniques for improving the ability of the integer programming approach to solve the verification problem for BNNs. Along the way, we hope to illustrate more generally some of the strategies integer programmers use to attack difficult problems like this. We find that our techniques enable verifying BNNs against a higher range of input perturbation than using the natural formulation directly.


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.
This is joint work with Woojin Kim, Mathematics PhD student at UW-Madison.


<div id="Zhdankin">
====Vladimir Zhdankin (UW)====
Title: Exploring astrophysical plasma turbulence with particle-in-cell methods


==== David Saintillan (UC San Diego) ====
Plasmas throughout the universe (as well as in the laboratory) tend to exist in turbulent, nonequilibrium states due to their "collisionless" nature. Described by the Vlasov-Maxwell equations in a six-dimensional phase space (of position and momentum), the basic physics of such plasmas is difficult to model from first principles. There remain open questions about entropy production, nonthermal particle acceleration, energy partition amongst different particle species, and more. Particle-in-cell simulations are a numerical tool that allow us to explore in depth the rich dynamics and statistical mechanics of collisionless plasmas, validating analytical speculation. I will describe some of the results from my group's work on this topic.


Title: Hydrodynamics of active nematic surfaces
<div id="Cockburn">
====Bernardo Cockburn (Minnesota)====
Title: Transforming stabilization into spaces


The dynamics of biological surfaces often involves the coupling of internal active processes with in-plane orientational order and hydrodynamic flows. Such active surfaces play a key role in various biological processes, from cytokinesis to tissue morphogenesis. In this talk, I will discuss two approaches for the modeling and simulation of active nematic surfaces. In a first model, we analyze the spontaneous dynamics of a freely-suspended viscous drop with surface nematic activity and its coupling with bulk fluid mechanics. Using a spectral boundary integral solver for Stokes flow coupled with a hydrodynamic evolution equation for the nematic tensor, numerical simulations reveal a complex interplay between the flow inside and outside the drop, the surface transport of the nematic field and surface deformations, giving rise to a sequence of self-organized behaviors and symmetry-breaking phenomena of increasing complexity, consistent with experimental observations. In the second part of the talk, I will present a novel computational approach for the simulation of active nematic fluids confined to Riemannian manifolds. The fluid velocity and nematic order parameter are represented as sections of the complex line bundle of a two-manifold. Using a geometric approach based on the Levi-Civita connection, we introduce a coordinate-free discretization method that preserves the continuous local-to-global theorems in differential geometry. Furthermore, we establish a nematic Laplacian on complex functions that can accommodate fractional topological charges through the covariant derivative on the complex nematic representation. Advection of the nematic field is formulated based on the Lie derivative, resulting in a stable geometric semi-Lagrangian discretization scheme for transport by the flow. The proposed surface-based method offers an efficient and stable means to investigate the influence of local curvature and topology on the hydrodynamics of active nematic systems, and we illustrate its capabilities by simulating active flows on a range of surfaces of increasing complexity.
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.


 
== Archived semesters ==
 
==== Rose Cersonsky (UW–Madison) ====
 
Title: Data-driven approaches to chemical and materials sciences: the importance of data selection, representation, and interpretability
 
Like many other fields, there has been a recent and overwhelming wave of machine learning and artificial intelligence methods being employed in the chemical sciences. While these methods have the undoubted ability to drive innovation and capabilities, their application to chemical sciences requires a nuanced understanding of molecular representations and structure-property relationships.
 
In this talk, I will discuss the role of molecular featurization – how we transform atoms and molecules into mathematical signals appropriate for machine-learning thermodynamic quantities – and unsupervised analyses that allow us to easily understand and assess these so-called “featurizations” in the context of complex machine learning tasks. In doing so, I will demonstrate how linear methods – that constitute the simplest, most robust, and most transparent approaches to automatically processing large amounts of data – can be leveraged to understand molecular crystallization and aid in pharmaceutical engineering.
 
All methods discussed are available through the open-source [https://scikit-matter.readthedocs.io scikit-matter] software, an official scikit-learn companion that implement methods born out of the materials and chemistry communities.
 
 
==== Rogerio Jorge (UW-Madison) ====
 
Title: The Direct Optimization Framework in Stellarator Design: Transport and Turbulence Optimization
 
Abstract:
When it comes to magnetic confinement nuclear fusion, high-quality magnetic fields are crucial for sustaining high-heat plasmas and managing plasma density, fast particles, and turbulence. Transport and turbulence are particularly important factors in this process. Traditional designs of stellarator machines, like those seen in the HSX and W7-X experiments, typically optimize magnetic fields and coils separately. This approach can result in limited engineering tolerances and often overlooks turbulent transport during the optimization process. Moreover, the process is highly dependent on the initial conditions, requiring multiple restarts with relaxed requirements, which can make it inefficient and compromise the optimal balance between alpha particles, neoclassical transport, and turbulence. However, recent breakthroughs in the optimization of stellarator devices are able to overcome such barriers. Direct near-axis designs, integrated plasma-coil optimization algorithms, precise quasisymmetric and quasi-isodynamic fields, and direct turbulence optimization are among the innovations that are revolutionizing the way these machines are designed. By taking into account transport and turbulence from the start, these advancements allow for more efficient fusion devices and greater control over the plasma. In this presentation, we will discuss the main outcomes of these advancements and the prospects for even more efficient and effective fusion devices.
 
 
==== Di Qi (Purdue) ====
Title: [[#diqi|Statistical Reduced-Order Models and Random Batch Method for Complex Multiscale Systems]]
 
Abstract: The capability of using imperfect stochastic and statistical reduced-order models to capture key statistical features in multiscale nonlinear dynamical systems is investigated. A systematic framework is proposed using a high-order statistical closure enabling accurate prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. A new efficient ensemble forecast algorithm is developed dealing with the nonlinear multiscale coupling mechanism as a characteristic feature in high-dimensional turbulent systems. To address challenges associated with closely coupled spatio-temporal scales in turbulent states and expensive large ensemble simulation for high-dimensional complex systems, we introduce efficient computational strategies using the so-called random batch method. It is demonstrated that crucial principal statistical quantities in the most important large scales can be captured efficiently with accuracy using the new reduced-order model in various dynamical regimes of the flow field with distinct statistical structures. Finally, the proposed model is applied for a wide range of problems in uncertainty quantification, data assimilation, and control.
 
 
==== Jinlong Wu (UW Madison) ====
Title: Operator learning for data-driven closure models of complex dynamical systems
 
Abstract: Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and Earth’s climate, for which direct numerical simulation that resolves all scales is often too expensive. For those systems without a clear scale separation, deterministic and local closure models often lack enough generalization capability, which limits their performance in many real-world applications. In this talk, I will present some recent efforts for constructing closure models that go beyond deterministic and local assumptions, based on (i) abundant direct data such as short temporal trajectories and (ii) a limited amount of indirect data (e.g., time-averaged statistics, physics constraints). Specifically, operator learning with direct and indirect data will be demonstrated in the context of both deterministic and stochastic closure modeling problems. The results show that the proposed methodology can leverage different types of data to construct advanced data-driven closure models, which potentially lead to better generalization capabilities than deterministic and local closures for modeling and simulation of complex dynamical systems.
 
==== Gabriel Zayas-Caban (UW Madison) ====
Title: Unveiling Bias in Sequential Decision Making: A Causal Inference Approach for Stochastic Service Systems
 
Abstract: In many stochastic service systems, decision-makers find themselves making a sequence of decisions, with the number of decisions being unpredictable. To enhance these decisions, it is crucial to uncover the causal impact these decisions have through careful analysis of observational data from the system. However, these decisions are not made independently, as they are shaped by previous decisions and outcomes. This phenomenon is called sequential bias and violates a key assumption in causal inference that one person's decision does not interfere with the potential outcomes of another. To address this issue, we establish a connection between sequential bias and the subfield of causal inference known as dynamic treatment regimes. We expand these frameworks to account for the random number of decisions by modeling the decision-making process as a marked point process. Consequently, we can define and identify causal effects to quantify sequential bias. Moreover, we propose estimators and explore their properties, including double robustness and semiparametric efficiency. In a case study of 27,831 encounters with a large academic emergency department, we use our approach to demonstrate that the decision to route a patient to an area for low acuity patients has a significant impact on the care of future patients.
 
 
'''Tony Kearsley (NIST)'''
 
Title: Control of inward solidification in Cryobiology
 
Abstract: For many years, mathematical models that predict a cell’s response to encroaching
 
ice has played an important role in developing cryopreservation protocols. It is clear
 
that information about the cellular state as a function of cooling rate can improve the
 
design of cryopreservation protocols and explain reasons for cell damage during freezing.
 
However, previous work has ignored the interaction between the important solutes, the
 
effects on the state of the cell being frozen and encroaching ice fronts. In this talk, I
 
will survey our work on this problem and examine the cryobiologically relevant setting
 
of a spherically-symmetric model of a biological cell separated by a ternary fluid mixture
 
from an encroaching solid–liquid interface and will illustrate our work on a simplified 1-D
 
problem. In particular, I will demonstrate how the thermal and chemical states inside the
 
cell are influenced and can potentially be controlled by altering cooling protocols at the
 
external boundary.
 
 
'''Malgorzata Peszynska''' '''(Oregon State University)'''
 
Title: Multiphysics across the scales for applications in permafrost
 
Abstract: We consider numerical solution based on P0-P0-RT0 fully implicit in time discretization of a coupled system of PDEs describing energy with thawing/freezing, flow and mechanical deformation in soils in permafrost regions, and aim to simulate the response of that system to environmental conditions such as warming surface conditions. One of the difficulties is the vastness of the Arctic system where the permafrost areas appear, and the related paucity of data. Towards this challenge we formulate an upscaling framework that can deliver the data at Darcy scale starting from xray micro-CT images of pore-scale geometries. This is joint work with many students and collaborators to be named in the talk.
 
== Future semesters ==


*[[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 04:11, 15 February 2025


Applied and Computational Mathematics Seminar


Spring 2025

Date Speaker Title Host(s)
Jan 31 Thomas Chandler (UW) Fluid–structure interactions in active complex fluids Spagnolie
Feb 7 Adrian Fraser (Colorado) Destabilization of transverse waves by periodic shear flows Spagnolie
Feb 14 Jim Luedtke (UW) Using integer programming for verification of binarized neural networks Spagnolie
Feb 21 Vladimir Zhdankin (UW) Exploring astrophysical plasma turbulence with particle-in-cell methods Spagnolie
Feb 28 Nick Boffi (CMU) TBA Li
Mar 7 Suraj Shankar (Michigan) TBA Spagnolie
Mar 14 Yue Lu (Harvard) [Colloquium] TBA Li
Mar 21 Genia Vogman (LLNL) TBA Li
Mar 28 Spring Break
Apr 4 TBA
Apr 11 Pierre Lermusiaux (MIT) TBA Chen
Apr 18 Jack Xin (UC Irvine) [Colloquium] TBA
Apr 25 Bernardo Cockburn (Minnesota) Transforming stabilization into spaces Stechmann, Fabien
May 2 Sylvia Herbert (UCSD) TBA Chen

Abstracts

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.

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.

Jim Luedtke (UW)

Title: Using integer programming for verification of binarized neural networks

Binarized neural networks (BNNs) are neural networks in which the weights are binary and the activation functions are the sign function. Verification of BNNs against input perturbation is one way to measure robustness of BNNs. BNN verification can be formulated as an integer linear optimization problem and hence can in theory be solved by state-of-the art methods for integer programming such as the branch-and-cut algorithm implemented in solvers like Gurobi. Unfortunately, the natural formulation is often difficult to solve in practice, even by the best such solvers, due to large integrality gap induced by its so-called "big-M" constraints. We present simple but effective techniques for improving the ability of the integer programming approach to solve the verification problem for BNNs. Along the way, we hope to illustrate more generally some of the strategies integer programmers use to attack difficult problems like this. We find that our techniques enable verifying BNNs against a higher range of input perturbation than using the natural formulation directly.

This is joint work with Woojin Kim, Mathematics PhD student at UW-Madison.

Vladimir Zhdankin (UW)

Title: Exploring astrophysical plasma turbulence with particle-in-cell methods

Plasmas throughout the universe (as well as in the laboratory) tend to exist in turbulent, nonequilibrium states due to their "collisionless" nature. Described by the Vlasov-Maxwell equations in a six-dimensional phase space (of position and momentum), the basic physics of such plasmas is difficult to model from first principles. There remain open questions about entropy production, nonthermal particle acceleration, energy partition amongst different particle species, and more. Particle-in-cell simulations are a numerical tool that allow us to explore in depth the rich dynamics and statistical mechanics of collisionless plasmas, validating analytical speculation. I will describe some of the results from my group's work on this topic.

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.

Archived semesters


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