Jun 7, 2024 · Approximation of dynamical systems by continuous time recurrent neural networks. ... A diffusion theory for deep learning dynamics: Stochastic gradient ...
3 days ago · Franco and A. Manzoni, Error estimates for POD-DL-ROMs: A deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper ...
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Jun 10, 2024 · Solving flows of dynamical systems by deep neural networks and a novel deep learning algorithm.
Jun 18, 2024 · Optimal control, in its broadest sense, examines the principle of optimization over dynamical systems. This methodological perspective naturally arises in ...
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Jun 19, 2024 · Lecture 1: introduction, deep learning as optimal control, dynamical systems and deep neural networks. Equivariant neural networks. • Lecture 2: Adversarial ...
Jun 2, 2024 · A novel deep learning approach for one-step conformal prediction approximation. ... Bayesian learning via stochastic gradient Langevin dynamics. In ...
Jun 10, 2024 · Knowl- edge integration into deep learning in dynamical systems: an overview and taxonomy. ... Approximation theory of the mlp model in neural net- works.
Jun 8, 2024 · Why study learning theory? Data have become ubiquitous in science, engineering, industry, and personal life, leading to the need for automated processing.
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Jun 17, 2024 · Data-driven modeling methods are studied for turbulent dynamical systems with extreme events under an unambiguous model framework. New neural network ...
1 day ago · Research Abstracts from Brunton Lab ; Deep Learning of Dynamics and Coordinates with SINDy Autoencoders · 52K views. 3 years ago ; Data-Driven Resolvent Analysis.
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