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pro vyhledávání: '"Wang, Thomas X"'
Solving time-dependent parametric partial differential equations (PDEs) is challenging, as models must adapt to variations in parameters such as coefficients, forcing terms, and boundary conditions. Data-driven neural solvers either train on data sam
Externí odkaz:
http://arxiv.org/abs/2410.03437
In this article, we introduce a novel normalization technique for neural network weight matrices, which we term weight conditioning. This approach aims to narrow the gap between the smallest and largest singular values of the weight matrices, resulti
Externí odkaz:
http://arxiv.org/abs/2409.03424
Publikováno v:
Conference on Neural Information Processing Systems (NeurIPS) 2024
We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent repre
Externí odkaz:
http://arxiv.org/abs/2406.02176
Autor:
Serrano, Louis, Boudec, Lise Le, Koupaï, Armand Kassaï, Wang, Thomas X, Yin, Yuan, Vittaut, Jean-Noël, Gallinari, Patrick
Machine learning approaches for solving partial differential equations require learning mappings between function spaces. While convolutional or graph neural networks are constrained to discretized functions, neural operators present a promising mile
Externí odkaz:
http://arxiv.org/abs/2306.07266