Search for tag: "acm seminar"

Title: Turbulence, Waves, and Stars Abstract: While stars are fluid bodies, the flows within them have long been shrouded in mystery. Recent space telescopes have allowed us to peer into stars in a…

Title: Physics-inspired Machine Learning Abstract: Combining physics with machine learning is a rapidly growing field of research.Thereby, most work focuses on leveraging machine learning methods to…

Title: Solitonic models of nonlinear phenomena: recent results and perspectives.Abstract: The concept of a diluted soliton gas was introduced in 1971 by V.E. Zakharov as an infinite collection of…

Title: Boundary Element Exterior CalculusAbstract: We consider first-kind boundary integral equations (BIEs) arising from boundary value problems for the Hodge-Laplacians and the Dirac operator…

Title: Modelling filtration through porous media: from domain decomposition towards digital twins.Abstract: Filtration through porous media is an important process occurring, e.g., in geophysics and…

Title: Exploring atomistic energy landscapes via bifurcation theory and numerical continuation techniques Abstract: Potential energy landscapes associated with atomistic modelling of matter are…

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…