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arXiv · 2410.04001

FastLRNR and Sparse Physics Informed Backpropagation

Woojin Cho·Kookjin Lee·Noseong Park·Donsub Rim·Gerrit Welper
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Abstract

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.

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Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit Welper. 2024-10-05. FastLRNR and Sparse Physics Informed Backpropagation. https://doi.org/10.1016/j.rinam.2025.100547

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