Abstract. We consider the propagation of acoustic waves in a 2D waveguide unbounded in one direction and containing a compact obstacle. The wavenumber is fixed so that only one mode can propagate.…

Abstract: Noether's First Theorem relates strictly invariant variational problems and conservation laws of their Euler--Lagrange equations. The Noether correspondence was extended by her…

The talk will start with a short introduction to the open-source Dedalus computational framework for solving PDEs. I'll briefly discuss some design motivations and interesting applied problems…

Abstract: The advent of new powerful deep neural networks (DNNs) has fostered their application in a wide range of research areas, including more recently in fluid mechanics. In this presentation, we…

Abstract: Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman operator…

Abstract: Many models in machine learning and PDE-based inverse problems exhibit intrinsic spectral properties, which have been used to explain the generalization capacity and the ill-posedness of…

Abstract: Mapping landforms on the surface of the Moon and the planets is important for resources exploration and future human settlements, and it presents interesting and difficult mathematical…

Abstract - The flow of a thin film down an inclined plane is a canonical setup in fluid mechanics and associated technologies, with applications such as coating, where the liquid-gas interface should…

Abstract: Semi-supervised learning is the problem of finding missing labels; more precisely one has a data set of feature vectors of which a (often small) subset are labelled. The semi-supervised…

Abstract: Many structured-output prediction (SOP) tasks in machine learning sport both soft (probabilistic) and hard (symbolic) constraints. Extending current deep learning architectures to correctly…

Abstract: Numerous real-world phenomena exhibit mechanisms evolving on greatly different time scales. In this talk, we focus on delayed loss of stability in two classes of mathematical models, namely…

Abstract: Partial Differential Equations (PDEs) are ubiquitous in the sciences and engineering. Although very successful, traditional numerical methods can be very expensive, even infeasible, for a…

Abstract: To solve a large-scale boundary value problem on a supercomputer, the problem domain is partitioned into smaller subdomains that can be tackled by individual processors. Since those…

Abstract: Complex dynamical systems like molecular systems or stochastic climate models often involve a variety of different time and length scales. We propose a strategy for coarse graining of such…

Abstract: The Hamiltonian Monte Carlo and Langevin processes are kinetic (position/velocity) Markov processes used to sampled target distributions in MCMC methods. In fact, once discretized in time,…

Abstract: Optimisation problems with discrete and continuous data appear in statistical estimation, machine learning, functional data science, robust optimal control, and variational inference. The…