Suspensions are composed of mixtures of particles and fluid and are
ubiquitous in industrial processes (e.g. waste disposal, concrete,
drilling muds, metalworking chip transport, and food processing) and in
natural phenomena (e.g. flows of slurries, debris, and lava). The
present talk focusses on the rheology of concentrated suspensions of
non-colloidal particles. It addresses the classical shear viscosity of
suspensions but also non-Newtonian behaviour such as normal-stress
differences and shear-induced migration. The rheology of dense
suspensions can be tackled via a diversity of approaches that are
introduced. In particular, the rheometry of suspensions can be
undertaken at an imposed volume fraction but also at imposed values of
particle normal stress, which is particularly well suited to yield
examination of the rheology close to the jamming transition. The
influences of particle roughness and shape are discussed.

Point cloud registration is the task of finding the transformation that aligns two data sets. We make the assumption that the data lies on a low-dimensional algebraic variety.  The task is phrased as an optimization problem over the special orthogonal group of rotations. We solve this problem using Riemannian optimization algorithms and show numerical examples that illustrate the efficiency of this approach for point cloud registration. 

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For high order finite elements (continuous piecewise polynomials) the conditioning of the basis is important. However, so far there seems no generally accepted concept of "a well-conditioned basis”,  or a general strategy for how to obtain such representations. In this presentation, we use the $L^2$ condition number as a measure of the conditioning, and construct representations by frames such that the associated $L^2$ condition number is bounded independently of the polynomial degree. The main tools include the bubble transform, which is a stable decomposition of functions into local modes, and orthogonal polynomials on simplexes.  We also include a brief discussion on potential applications in preconditioning. This is a joint work with Ragnar Winther. 

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Motivated by the work of K.R. Rajagopal, the objective of the talk is to discuss the construction and analysis of numerical approximations to a class of models that fall outside the realm of classical Cauchy elasticity. The models under consideration are implicit and nonlinear, and are referred to as strain-limiting, because the linearised strain remains bounded even when the stress is very large, a property that cannot be guaranteed within the framework of classical elastic or nonlinear elastic models. Strain-limiting models can be used to describe, for example, the behavior of brittle materials in the vicinity of fracture tips, or elastic materials in the neighborhood of concentrated loads where there is concentration of stress even though the magnitude of the strain tensor is limited.

We construct a finite element approximation of a strain-limiting elastic model and discuss the theoretical difficulties that arise in proving the convergence of the numerical method. The analytical results are illustrated by numerical experiments.

The talk is based on joint work with Andrea Bonito (Texas A&M University) and Vivette Girault (Sorbonne Université, Paris).

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Often in scattering applications it is advantageous to reformulate the problem as an integral equation, discretize, and then solve the resulting linear system using a fast direct solver. The computational cost of this approach is typically dominated by the work needed to compress the coefficient matrix into a rank-structured format. In this talk, we present a novel technique which exploits the bandlimited-nature of solutions to the Helmholtz equation in order to accelerate this procedure in environments where multiple frequencies are of interest.

This talk is based on joint work with Gunnar Martinsson (UT Austin).

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Computer-based simulation of partial differential equations (PDEs) involves approximating the unknowns and relies on suitable description of geometrical entities such as the computational domain and its properties. The Finite Element Method (FEM) is by large the most popular technique for the computer-based simulation of PDEs and hinges on the assumption that discretized domain and unknown fields are both represented by piecewise polynomials, on tetrahedral or hexahedral partitions. In reality, the simulation of PDEs is a brick within a workflow where, at the beginning, the geometrical entities are created, described and manipulated with a geometry processor, often through Computer-Aided Design systems (CAD), and then used for the simulation of the mechanical behaviour of the designed object. This workflow is often repeated many times as part of a shape optimisation loop. Within this loop, the use of FEM on CAD geometries (which are mainly represented through their boundaries) calls then for (re-) meshing and re-interpolation techniques that often require human intervention and result in inaccurate solutions and lack of robustness of the whole process. In my talk, I will present the mathematical counterpart of this problem, I will discuss the mismatch in the mathematical representations of geometries and PDEs unknowns and introduce a promising framework where geometric objects and PDEs unknowns are represented in a compatible way. Within this framework, the challenges to be addressed in order to construct robust PDE solvers are many and I will discuss some of them. Mathematical results will besupported by numerical validation.

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Data scientists are often faced with the challenge of understanding a high dimensional data set organized as a table. These tables may have columns of different (sometimes, non-numeric) types, and often have many missing entries. In this talk, we discuss how to use low rank models to analyze these big messy data sets. Low rank models perform well --- indeed, suspiciously well — across a wide range of data science applications, including applications in social science, medicine, and machine learning. In this talk, we introduce the mathematics of low rank models, demonstrate a few surprising applications of low rank models in data science, and present a simple mathematical explanation for their effectiveness.

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