Numerical aspects of integration in physics-informed neural networks modelling
<|reference_start|>Numerical aspects of integration in physics-informed neural networks modelling: In this paper we numerically analyse problems that arise in numerical integration used in various problems that are nowadays being solved by physics-informed neural networks. In particular we show how inaccurately evaluated integrands can lead to severe precision problems. The tricky and serious part of the problem is especially in its detection, when for example a simple macroscopic visualization of the integrand does not indicate any potential problems and obtained results are often wrongly treated as correct. To solve the problem, it is crucial to decide if it is sufficient to evaluate the integrand in the standard double arithmetic or if a higher precision arithmetic has to be used. We compare and recommend numerical quadratures for selected examples and different parameter values, especially for problematic cases.<|reference_end|>
@article{daněk2024numerical,
    title={Numerical aspects of integration in physics-informed neural networks
  modelling},
    author={Josef Danv{e}k and Jan Posp'iv{s}il},
    journal={arXiv preprint arXiv:2408.05172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05172},
    primaryClass={math.NA cs.NA}
}