FastLRNR and Sparse Physics Informed Backpropagation
<|reference_start|>FastLRNR and Sparse Physics Informed Backpropagation: 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.<|reference_end|>
@article{cho2024fastlrnr,
    title={FastLRNR and Sparse Physics Informed Backpropagation},
    author={Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit Welper},
    journal={arXiv preprint arXiv:2410.04001},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04001},
    primaryClass={cs.LG cs.AI cs.NA math.NA}
}