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…

OPENspace Research Seminar by Dr. Stephen Malden on 14.03.2023

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…

OPENspace Research Seminar - Leyla Deniz Kiraz 08.11.2023

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…

In this final video, we offer suggestions for how to prepare for the thematic readings and discussions