FastLRNR and Sparse Physics Informed Backpropagation

Autor:	Cho, Woojin, Lee, Kookjin, Park, Noseong, Rim, Donsub, Welper, Gerrit
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Mathematics - Numerical Analysis 68T07 65D25 65M22
Druh dokumentu:	Working Paper
Popis:	We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR). The approach exploits the low rank structure within LRNR and constructs a reduced neural network approximation that is much smaller in size. We call the smaller network FastLRNR. We show that backpropagation of FastLRNR can be substituted for that of LRNR, enabling a significant reduction in complexity. We apply SPInProp to a physics informed neural networks framework and demonstrate how the solution of parametrized partial differential equations is accelerated. Comment: 10 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.04001 Zobrazit plný text záznamu View this record from Arxiv