Lyapunov weights to convey the meaning of time in physics-informed neural networks
<|reference_start|>Lyapunov weights to convey the meaning of time in physics-informed neural networks: Time is not a dimension as the others. In Physics-Informed Neural Networks (PINN) several proposals attempted to adapt the time sampling or time weighting to take into account the specifics of this special dimension. But these proposals are not principled and need guidance to be used. We explain here theoretically why the Lyapunov exponents give actionable insights and propose a weighting scheme to automatically adapt to chaotic, periodic or stable dynamics. We characterize theoretically the best weighting scheme under computational constraints as a cumulative exponential integral of the local Lyapunov exponent estimators and show that it performs well in practice under the regimes mentioned above.<|reference_end|>
@article{turinici2024lyapunov,
    title={Lyapunov weights to convey the meaning of time in physics-informed
  neural networks},
    author={Gabriel Turinici},
    journal={arXiv preprint arXiv:2407.21642},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21642},
    primaryClass={cs.LG cs.AI cs.NA math.NA}
}