Scientific Computing Seminar
Division of Applied Mathematics

Date: Friday, September 26

Speaker: Beatrice Riviere (Rice)

Title: 

Abstract:  

Date: Friday, April 11

Speaker: Ioannis  Papadopoulos, Weierstrass Institute in Berlin.

Title: Hierarchical proximal Galerkin: a fast hp-FEM solver for variational problems with pointwise inequality constraints. 

Abstract:   High-order discretizations for problems with pointwise constraints are often incompatible with traditional solvers or lead to slow solve times.

In 2024 a novel algorithm, called the latent variable proximal point method (LVPP), was proposed for solving PDE-constrained optimization problems with pointwise inequality constraints. It is formulated on the infinite-dimensional level and enjoys many of the favourable characteristics of a semismooth Newton method including mesh independence and provable fast convergence rates. Moreover, no special considerations are required for its discretization allowing one to use any number of techniques such as finite volume, spectral methods, PINNs, or, as we will explore in this talk, hp-finite element methods (hp-FEM).

We will explore the use of LVPP for the obstacle problem (an obstacle-type constraint), the generalized elastic-plastic torsion problem (a gradient-type constraint), and the thermoforming problem (an obstacle-type quasi-variational inequality) where we observe an hp-independent number of iterations to reach convergence. By leveraging the hierarchical p-FEM basis, we will consider discretizations up to pre-tensor degree p=82, which far exceed any previously reported discretizations for such problems. Moreover, by utilizing fast transforms of the FEM basis for assembly and preconditioned iterative solvers for the solve, we achieve wall-clock times that are up to 100 times faster to attain a target error than low order counterparts.

Preprint: https://arxiv.org/abs/2412.13733

Date: Friday, March 28

Speaker: Mohsen Zayernouri, Michigan State University

Title:Probabilistic Consequences of Heavy Tails onNumerical Analysis and Scientific Computing/Learning 

Abstract:  

Nature remembers and is abundant with processes that exhibit heavy-tail distributions. The probabilistic

consequences of such tails entail informative details, whose proper incorporations can directly contribute

to new phenomena discoveries. This can happen via extracting and exploiting intrinsic features from

(Lagrangian/Eulerian) data and by means of two-way linkage between the microscopic motions and their

collective/continuum dynamics. To this end, we first tackle the only major unsolved problem in classical

physics, i.e., Flow/Scalar Turbulence. By performing a novel statistical analysis and introducing a new

nonlocal length scale in the inertial-convective subrange which codes intermittency in the jump processes,

we derive a new spectral scaling law for the cascade of scalar variance that remarkably aligns with data.

Next, we mathematically show that this spectral law in turn leads to a new nonlocal dynamic subgrid-scale

(SGS) model for the large-eddy simulation (LES) of scalar turbulence, surpassing the current state-of-the-

art and dynamic LES approaches in an unprecedented way. Moreover, by framing the flow turbulence

‘Closure Problem’ at the Boltzmann’s kinetic theory of interacting particles, we derive a new class of

turbulence closure models at the continuum flow level, called “Dynamic Tempered Fractional SGS Model”,

rigorously emerging as a tempered fractional Laplacian operator in the filtered Navier-Stokes’ equations.

This expressive and data-friendly tempered LES model adeptly captures several anomalous statistical

features such as: heavy tails and pronounced sharp peaks in the dissipation distributions, in addition to

accurate prediction of both forward cascade and backscattering of the turbulent kinetic energy. Therefore,

the proposed multi-scale (kinetics-to-continuum) LES modeling paradigm paves the way for developing a

new generation of intelligent nonlocal turbulence models for long-standing and large-scale applications.

On the numerical analysis side, the k-fold cross-validation of nonlocal convective/advective-diffusive

models often introduces a range of solution singularities, rendering the underlying Sobolev spaces’

fractional indices random. We first review some major developments in fractional and tempered fractional

Sturm-Liouville eigen-problems, providing tunable basis/test functions with attractive spectral properties.

Subsequently, we introduce our notion of “Distributed Sobolev Spaces”, which helps rigorously obtain the

corresponding variational formulation for the resulting distributed PDEs. We will next present a variant of

Petrov-Galerkin spacetime spectral method for fully distributed advection-diffusion-reaction PDEs along

with a unified fast solver with optimal complexity in any (1+d) dimensions. We finally reveal several new

exciting research fronts, where our proposed stochastic multi-scale approach in discovering predictive

generalized-order models appear to make a noticeable difference in the context of complex/polymeric and

aging materials in energy, defense, and health applications.

Date: Friday, March 14

Speaker:  Xiangxiong Zhang  (Purdue)

Title: An optimization-based approach for enforcing invariant domain of gas dynamics equations 

Abstract: 

Date: Friday,  February 28  (no double seminar as the room is booked)

Speaker: Boris Andrews (Oxford)

Title: High-order conservative and accurately dissipative numerical integrators via auxiliary variables 

Abstract:  Numerical methods for the simulation of transient systems with structure-preserving properties are known to exhibit greater accuracy and physical reliability, in particular over long durations.

However, there remain difficulties in devising geometric numerical integrators that conserve non-quadratic invariants or dissipation laws.

In this work, we propose a framework for the construction of timestepping schemes that preserve dissipation laws and conserve multiple general invariants, via finite elements in time and the systematic introduction of auxiliary variables; each of these methods extends to arbitrary order in time.

We demonstrate the ideas by devising novel integrators that exactly conserve all known invariants of general integrable systems (including the Kepler and Kovalevskaya problems) and finite-element schemes for the compressible Navier-Stokes equations that conserve mass, momentum, and energy, and provably possess non-decreasing entropy. The approach further generalises several existing ideas in the literature, including Gauss methods, the framework of Cohen & Hairer, and the energy- and helicity-conserving scheme of Rebholz. 

Date: Friday,  February 28  (no double seminar as the room is booked)

Speaker: Boris Andrews (Oxford)

Title: High-order conservative and accurately dissipative numerical integrators via auxiliary variables 

Abstract:  Numerical methods for the simulation of transient systems with structure-preserving properties are known to exhibit greater accuracy and physical reliability, in particular over long durations.

However, there remain difficulties in devising geometric numerical integrators that conserve non-quadratic invariants or dissipation laws.

In this work, we propose a framework for the construction of timestepping schemes that preserve dissipation laws and conserve multiple general invariants, via finite elements in time and the systematic introduction of auxiliary variables; each of these methods extends to arbitrary order in time.

We demonstrate the ideas by devising novel integrators that exactly conserve all known invariants of general integrable systems (including the Kepler and Kovalevskaya problems) and finite-element schemes for the compressible Navier-Stokes equations that conserve mass, momentum, and energy, and provably possess non-decreasing entropy. The approach further generalises several existing ideas in the literature, including Gauss methods, the framework of Cohen & Hairer, and the energy- and helicity-conserving scheme of Rebholz. 

Date: Friday,  February 21 

Speaker: Phillipe Devloo  (UNICAMP)
Prof. Devloo studied his undergraduate studies in Gent, Belgium in Electromechanical Engineering (1976 - 1981). After a one-year study of Computer Science in 1981, he enrolled at the University of Texas in Austin to pursue a PhD in computational mechanics under the supervision of J T Oden (1982 - 1987). From 1988-1922 worked at INPE (Space Research Institute in Brasil). From 1992-now is full professor at the State University of Campinas.  

Title:  Research on Hdiv approximations and their use in computational mechanics 

Abstract:  In recent years the research group of LabMeC/UNICAMP has developed innovative research in Hdiv approximations. This seminar emphasizes the

different steps of this research effort.

- An innovative construction of Hdiv spaces for the complete family of finite element topologies, with De Rham compatibility to corresponding L2 spaces or Hcurl spaces

- Effective error estimation of Hdiv approximations using H1 reconstruction

- Construction of high order Hdiv approximations with constant divergence

- hp-adaptive Hdiv approximations, a criterium for refining in h or p

- Pointwise conservating Hdiv approximations in a multiscale setting

- On the use of Hdiv spaces for approximating Stokes and/or elasticity equations 

Date: Friday,  February 21, Noon-1:00pm

Speaker:   Johannes Brandstetter  ,Johannes Kepler University Linz and serves as Chief Researcher at NXAI 

Title:  What’s the next wave of disruption in science and engineering?

Abstract: 

In the era of LLM models, one gets notoriously confronted with the question of where we stand with applicability of large-scale deep learning models within scientific or engineering domains. The discussion starts by reiterating on recent triumphs in weather and climate modeling, making connections to computer vision, physics-informed learning and neural operators. Secondly, we discuss (potential) breakthroughs in multi-physics modeling, computational fluid dynamics, plasma physics, and general relativity.

Date: Friday,  January 31, 2025

Speaker:  Vaclav Kucera (Charles University)

Title: Tempered Finite Elements: A novel method for computing on degenerate meshes 

Abstract:  Classical finite elements fail to converge when the triangulation of

the computational domain contains too many degenerating elements organized in

large structures. Such triangulations are however important from the practical

point of view, e.g. when meshing interfaces. In this talk we present a simple

modification of the finite element method which allows to compute on meshes

containing arbitrarily degenerate elements, even ones with zero or negative

measure. We present the theory as well as practice of this new technique and demonstrate its performance on various problems. 

Date: Friday, November 1 11am-noon OK; noon-1pm NOT OK (room is booked)

Speaker:  

Date: Friday, November 1 11am-noon OK; noon-1pm NOT OK (room is booked)

Speaker:  

Date: Friday, October 25, 2024

Speaker:  Wei Wang (University of Florida)

Title:  Multiscale Discontinuous Galerkin Method for Schrodinger Equations 

Abstract:  In this talk, we will introduce a high-order multiscale discontinuous Galerkin (DG) method for stationary Schrodinger

equations in quantum transport. Due to the oscillatory nature of the solutions, traditional numerical methods require extremely

refined meshes to capture the small scale structure, resulting in the high computational cost. Our multiscale method applies WKB asymptotic to construct non-polynomial multiscale basis functions so that it can capture the solution better on coarse meshes. We will first present our approach for the 1D case and then extend our approach to a special 2D case where the solution exhibits oscillations mainly in one direction. Numerical results and error estimates will also be provided.

Joint work with Prof. Bo Dong and Prof. Chi-Wang Shu. 

Date: Friday, October 11, 2024

Speaker: Andreas Meister of University of Kassel
Title:  Asymptotic Analysis and Numerical Methods for Compressible Flows at all Mach Numbers

Abstract:  We will present a comprehensive study of a finite volume method for inviscidand viscous flow fields at high and low speeds. Thereby, the results of a formal asymptotic low Mach number analysis are used to extend the validity of the numerical method from the simulation of compressible flow fields at transonic as well as supersonic speed to the low Mach number regime. To overcome the well-known failure of compressible numerical method in the low Mach number regime we combine the numerical flux function with a preconditioned formulation. Both,a wide variety of trans-, super-, hyper-, and subsonic realistic test cases as wellas a formal discrete asymptotic analysis are employed in order to prove the validity of the derived numerical method from hypersonic to low Mach numberfluid flow.

Date: Friday, September 13, 2024

Speaker: Adi Ditkowski  

Title: Department of Applied Mathematics School of Mathematical Sciences, Tel Aviv University,  

Abstract: Block Finite Difference methods (BFD) are Finite Difference (FD) methods in which the domain is divided into blocks, or cells, containing two or more grid points with a different stencil used for each grid point, unlike the standard FD method, where the same stencil is used for every grid point. Using this approach, we can design the scheme such that the leading term of the truncation error lies in a different subspace than the solution. Different dynamics can then be assigned to the error and the solution. Therefore, we can construct high-performance schemes.

In this talk, we present BFD for the heat and advection equations in one and multiple dimensions. For the heat equations, we show that we can use the scheme's dissipation to get a fifth-order convergence rate from a third-order truncation scheme. Furthermore, a six-order convergence rate can be obtained using a post-processing filter.

We can obtain a six-order phase error for the advection equation while maintaining the rest of the errors bounded in time.

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We also show that these BFD schemes are highly accurate, nodal-based Discontinuous Galerkin  Methods.

This work was conducted in collaboration with Anne Le Blanc and Chi-Wang Shu.

Date: Friday, October 6, 2023 

Speaker:  Jesse Chan Rice University

Title: 

Abstract:

Date: Friday, November 10, 2023 AT Noon! 

Speaker:  Tarik Dzanic

Title: Constructing provably robust, constraint-satisfying finite element methods for computational physics

Abstract: Finite element methods (FEM) have been widely used for simulating a broad range of multi-scale, multi-physics phenomena due to their accuracy and computational efficiency. However, when approximating systems that must abide by strict physical constraints, their robustness can be severely degraded as the predicted solutions may not satisfy these constraints, resulting in physically inconsistent predictions or the failure of the schemes altogether. In this talk, I will present provably robust, constraint-satisfying FEM approaches for computational physics applications, particularly focusing on discontinuous FEM-type schemes for hyperbolic and mixed systems. The proposed methodology relies on constructing and enforcing specific constraints on the solution such that the schemes can: 1) guarantee that the predicted solution remains physically admissible; 2) robustly and accurately resolve features that may otherwise cause numerically ill-behaved solutions; and 3) retain the high fidelity, high-order accuracy, and computational efficiency of standard FEM approaches. This talk will discuss how physical admissibility constraints can be paired with more restrictive entropy conditions that ensure well-behaved numerical solutions without sacrificing accuracy and how these conditions can be enforced through nonlinear filtering methods, which can be efficiently implemented on modern massively-parallel computing architectures. The resulting approach will be shown across a variety of application areas, ranging from multi-species gas dynamics and viscous fluid flows to magnetohydrodynamics and statistical thermodynamics.

Date: Friday, February 2, 2024 

Speaker:  Paris Perdrekis (U-PENN)

Title: A Unifying Framework for Operator Learning via Neural Fields 

Abstract: Operator learning is an emerging area of machine learning which aims to learn mappings between infinite dimensional function spaces and has led to the development of new architectures such as the Fourier Neural Operator, the DeepONet, and their extensions.  In this talk I will uncover a previously unrecognized connection between existing operator learning architectures and conditioned neural fields used in computer vision.  This results in a unified framework for explaining differences between popular operator learning architectures, and creates a bridge for adapting well-developed tools from computer vision for operator learning.  In particular, we find all existing operator learning architectures are neural fields whose conditioning mechanisms are restricted to use only pointwise and/or global information from their inputs.  This motivates us to design new architectures which make use of a hierarchy of scales for conditioning a base neural field.  By making use of multi-scale conditioning, we observe consistent performance gains and obtain state of the art results across a collection of challenging benchmarks in climate modelling and fluid dynamics.

*joint work with Jacob Seidman, Hanwen Wang, Shyam Sankaran and George Pappas

Date: Friday, February 9, 2024 

Speaker:  Andre  Nachbin, Worcester Polytechnic Institute

Title: Solitary Water Waves on Graphs  

Abstract: We have deduced a weakly nonlinear, weakly dispersive Boussinesq system for water waves on a 1D branching channel, namely on a graph. The model required a new compatibility condition at the graph’s node, where the main reach bifurcates into two reaches. The new nonlinear compatibility condition generalizes that presented by Stoker (1957) and includes forking angles. We present numerical simulations comparing solitary waves on the 1D (reduced) graph model with results of the (parent) 2D model, where a compatibility condition is not needed. We will comment on some new problems that arise.  

Date: Friday, February 16, 2024 

Speaker:  Daniel Appelo (Virginia Tech)

Title: WaveHoltz: Parallel and Scalable Solution of the Helmholtz Equation via Wave Equation Iteration  

Abstract: We introduce a novel idea, the WaveHoltz iteration, for solving the Helmholtz equation. The method is inspired by recent work on exact controllability (EC) methods and as in EC methods we make use of time domain methods for wave equations to design frequency domain Helmholtz solvers, but unlike EC methods we do not require adjoint solves. We show that the WaveHoltz iteration is symmetric and positive definite (compared to the indefinite Helmholtz equation). We present numerical examples, using various discretization techniques, that show that our method can be used to solve problems with rather high wave numbers.   

Date: Friday, February 23, 2024 

Speaker:    Bamdad Hosseini, Washington University

Title:  Kernel Methods for Solving and Learning PDEs: Algorithms and Error Analysis 

Abstract: Abstract: In this talk I will present a kernel collocation method for solving PDEs and inverse problems that is inspired by advances in the machine learning literature and in particular the theory of Reproducing Kernel Hilbert Spaces (RKHSs) and Gaussian Processes (GPs). The proposed methodology has a lot of desirable properties such as a dimension benign implementation and relatively straightforward error analysis. I will discuss the main idea behind this error analysis and how it reveals a broader framework for the analysis of other PDE solvers including neural net techniques such as Physics Informed Neural Nets (PINNs). 

Date: Friday, March 8, 2024 

Speaker: . Yingda Cheng, Virginia Tech  

Title: Implicit time and rank adaptive method for time-dependent PDEs 

Abstract: In this work, we develop implicit time- and rank-adaptive schemes for stiff time-dependent matrix differential equations. The idea of dynamic low rank approximation (DLRA) is to capture the dynamic evolution of the low rank matrices by a numerical procedure. Our scheme is based on a three-step procedure similar to the unconventional robust integrator (BUG integrator). First, a prediction step is made computing the approximate column and row spaces at the next time level. Second, a Galerkin evolution step is invoked using a base implicit solve for the small core matrix. Finally, a truncation is made according to the error threshold. However, compared with the BUG integrator, which has convergence issues for some PDEs with cross terms, our scheme has two important features which enhance the robustness for convergence. We use the row and column spaces from the explicit step truncation method in the prediction step to alleviate the tangent projection error associated with the dynamic low rank approximation. In addition, a time adaptive strategy is proposed in conjunction with rank adaptivity. We benchmark the scheme in several tests such as anisotropic diffusion to show robust convergence properties. 

Date: Friday, March 29, 2024 

Speaker: Thomas Surowiec, Simula Research Laboratory

Title: Proximal Galerkin: A structure-preserving numerical method for constrained variational problems. From the obstacle problem to fully nonlinear PDEs

Abstract: Bound constraints are one of the most natural features to include in a physical model. Water should not flow through solid rock, warm plastic bends to its mold, and granular material like snow and sand has a natural angle of repose. A computational model can account for these properties by imposing bound constraints on the space of solutions. However, this natural modeling assumption has a steep price. At the theoretical level, it propels us from the realm of partial differential equations (PDEs) to the more intricate domain of infinite-dimensional variational inequalities (VIs). Meanwhile, at the computational level, we meet difficulties overcoming challenges with mesh-dependence, high iteration complexity, and low accuracy that do not appear in unconstrained numerical models.

By combining notions from convex analysis, mathematical optimization, and mixed finite element methods, we propose a new type of nonlinear, geometry-preserving finite element method for variational inequalities, optimal control problems, and several challenging classes of fully nonlinear PDEs. We call this approach the "Proximal Galerkin Method."

With the classical obstacle problem serving as the canonical example for pointwise bound-constrained elliptic variational inequalities, we systematically motivate the method from the perspective of solvers for mixed complementarity problems arising in optimization. After introducing two classes of specially tailored finite element spaces, we investigate the method's performance over various meshes and polynomial degrees, where we observe strong mesh-independence, low iteration complexity, and high order accuracy. We also consider the method's behavior on degenerate problems and its ease of implementation, even when using higher-degree basis functions. These results are complemented by examples in topology optimization and advection-diffusion. The second part of the talk is focussed on both the theoretical underpinnings of the method as well as extensions to fully nonlinear PDEs; in this case, the Eikonal and Monge-Ampère equations.

The proximal Galerkin method was discovered in collaboration with Brendan Keith (Brown).

The results of this talk are part of joint, ongoing work with Brendan Keith, Jørgen Dokken (Simula Research Laboratory), and Patrick E. Farrell (Oxford). 

Date: Friday, April 12, 2024

Speaker: ROOM IS BOOKED FOR DEFENSE

Title: 

Abstract:

Date: Friday, April 19, 2024

Speaker: Paula Chen (Naval Air Warfare Center Weapons Division (NAWCWD), China Lake)

Title: Recent Advancements in Designing FPGA Implementations for Real-Time Optimal Control

Abstract: Field programmable gate arrays (FPGAs) represent a promising hardware architecture for real-time, embedded applications. In appropriate settings, FPGAs have been shown to be able to achieve 10-100x speedup over CPUs and GPUs, while using less size, weight, and power. In this talk, we provide an overview of recent advancements and open challenges in designing real-time FPGA implementations for optimal control algorithms. In particular, we discuss how high-level synthesis software tools can be leveraged to facilitate this design process.

Date: Friday, April 26, 2024:

Speaker: Charlie Parker (Oxford University)

Title: Computing H2-conforming finite element approximations without having to implement C1-elements 

Abstract: Fourth-order elliptic problems arise in a variety of applications from thin plates to phase separation to liquid crystals. A conforming Galerkin discretization requires a finite dimensional subspace of H2, which in turn means that conforming finite element subspaces are C1-continuous. In contrast to standard H1-conforming C0-elements, C1-elements, particularly those of high order, are less understood from a theoretical perspective and are not implemented in many existing finite element codes. In this talk, we address the implementation of the elements. In particular, we present algorithms that compute C1 finite element approximations to fourth-order elliptic problems and which only require elements with at most C0-continuity. We also discuss preconditioners and illustrate the method on a number of representative test problems. 

Date: Friday, May 3, 2024:

Speaker:   Florian Schaefer (Georgia Tech)

Title: Models, Solvers, Learners: Statistical Inspiration for Scientific Computing

Abstract: The convergence of scientific computing with statistics and machine learning is an exciting recent development. In this talk, I will present two lines of work that blur the line between statistical inference and numerical computation.

The first and main part of the talk uses ideas from semidefinite programming and information geometry to efficiently simulate gas dynamics in the presence of shock waves. The latter cause severe numerical challenges for classical and learning-based solvers.

The talk begins by observing that shock formation arises from the deformation map reaching the boundary of the manifold of diffeomorphisms. This motivates using the log-determinant barrier function of semidefinite programming to modify the geometry of the manifold such that the deformation map approaches but never reaches its boundary. This information geometric regularization (IGR) preserves the original long-time behavior without forming singular shocks, greatly simplifying numerical simulation. The modified geometry on the diffeomorphism manifold is also the information geometry of the mass density. I will show how this observation motivates information geometric mechanics that views the solutions of continuum mechanical equations as parameters of probability distributions to be evolved on a suitable information geometry, promising far-reaching extensions of IGR. 

The second part of the talk derives fast solvers for elliptic PDEs by relating Cholesky factorization to the conditional distributions of a Gaussian process. The resulting algorithms achieve the new state of the art in terms of rigorous accuracy-vs-cost guarantees, are readily parallelizable, and performant in practice. 

Date: Friday,May 10, 2024:

Speaker:  Leszek Demkowicz (UT Austin)

Title: Separation of Variables with Non-Self Adjoint Operators with Applications to Analysis of Waveguides 

Abstract: I have been teaching separation of variables for over 30 years, and I have always been instructing students

to look for a self-adjoint problem first. We know then that the separation constant (the eigenvalue) is real

and, if the operator happens to be positive definite, we also know that the constant is positive.

This a-priori knowledge about the separation constant simplifies greatly the solution of the problem.

More importantly, by the Sturm-Liouville theory (Spectral Theorem for Self-Adjoint Operators), we know

that the eigenvectors form simultaneously a basis for the L2 as well H1 space, which opens up the way

for a rigorous well-posedness proof.

Only recently, when studying acoustical and electromagnetic waveguides, we have run into a situation where

one needs to perform the separation of variables with a non-self adjoint operator. This has led me to

a half-year long study of excellent and fundamental book by Gohberg and Krein [1] and a number of fundamental

results that I have learned (and should have known a long time ago).

In the talk, I will illustrate the deep theory for non-self adjoint operators with a simple model acoustic

waveguide problem with impedance boundary conditions.

[1] I. Gohberg and M. Krein, Introduction to the Theory of Linear Nonselfadjoint Operators in Hilbert

Space (Translations of Mathematical Monographs). American Mathemtical Society, 1965

(Russian edition), vol. 18.

[2] M. Melenk, L. Demkowicz, and S. Henneking, ‘‘Stability analysis for electromagnetic

waveguides. Part 1: Acoustic and homogeneous electromagnetic waveguides.,’’ Oden

Institute, The University of Texas at Austin, Austin, TX 78712, Tech. Rep. 2, 2023, in review.

[3] L. Demkowicz, M. Melenk, J. Badger, and S. Henneking, ‘‘Stability analysis for acoustic and

electromagnetic waveguides. Part 2: Non-homogeneous waveguides.,’’ Advances in

Computational Mathematics, 2024, accepted, see also Oden Institute Report 2023/3. 

Date: Friday, Feb., 10, 2023 

Speaker: Stefanie Guenther, Lawrence Livermore National Laboratory

Title:   Optimal control of open quantum systems on HPC platforms

Abstract:

Date: Friday, Feb. 24, 2023  

Speaker: Hasnaa Zidani (INSA Rouen) and Olivier Bokanowski (Laboratoire Jacques-Louis Lions, Sorbonne universite)

Title:   TBA 

Abstract: 

Date: Wednesday, April 12, 2023  

Speaker: Marie Elisabeth Rognes, Simula Research Laboratory

Title:   Perivascular pathways and the dimension-2 gap

Abstract:

Date: Friday, Nov. 18, 2022

Speaker: Jay Gopalakrishnan, Portland State University

Title: A diagram chase to get a discrete elasticity complex

Abstract: