Search for tag: "acm seminar"

26/04/2023: Lucas Chesnel - Acoustic passive cloaking using thin outer resonators

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.…

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19/04/2023: Peter Olver - Two New Developments in Noether's Two Theorems

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…

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29/03/2023 Geoff Vasil: Tensors and polynomials, for fun and profit

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…

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01/02/2023 Ricardo Vinuesa: Sensing and control of turbulent flows through deep learning

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…

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23/11/2022 Stefan Klaus: Data-driven analysis of complex dynamical systems

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

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16/11/2022 Yunan Yang: Benefits of Weighted Training in Machine Learning and PDE-based Inverse Problems

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…

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09/11/2022 Klaus Mosegaard: Planetary surface mapping as an inverse problem

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…

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(02/11/2022) Susana Gomes: Control of falling liquid films using a hierarchical model approach

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…

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(26/10/2022) Matt Thorpe: A Random Walk from Semi Supervised Learning to Neural Networks

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…

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(19/10/2022) Antonio Vergari: Semantic Probabilistic Layers for Neuro-Symbolic Learning

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…

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(14/10/2022) Mattia Sensi: Delayed loss of stability in multiple time scale models of natural phenomena

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…

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(05/10/2022) Sid Mishra: Deep Learning and computations of PDEs

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…

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(28/09/2022) Francisco Bernal: Taming Amdahl's "curse" with probabilistic domain decomposition

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…

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(09/03/2022) Carsten Hartmann: Coarse-graining of multiscale diffusions with parameter uncertainties

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…

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(02/03/2022) Pierre Monmarché: Non-asymptotic convergence bounds for Hamiltonian Monte Carlo and Langevin diffusion in the convex case

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,…

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(09/02/2022) Jonas Latz: Stochastic gradient descent in continuous time: discrete and continuous data

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

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