Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs
<|reference_start|>Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs: Physics-informed neural networks (PINNs) provide a means of obtaining approximate solutions of partial differential equations and systems through the minimisation of an objective function which includes the evaluation of a residual function at a set of collocation points within the domain. The quality of a PINNs solution depends upon numerous parameters, including the number and distribution of these collocation points. In this paper we consider a number of strategies for selecting these points and investigate their impact on the overall accuracy of the method. In particular, we suggest that no single approach is likely to be "optimal" but we show how a number of important metrics can have an impact in improving the quality of the results obtained when using a fixed number of residual evaluations. We illustrate these approaches through the use of two benchmark test problems: Burgers' equation and the Allen-Cahn equation.<|reference_end|>
@article{florido2024investigating,
    title={Investigating Guiding Information for Adaptive Collocation Point
  Sampling in PINNs},
    author={Jose Florido, He Wang, Amirul Khan, Peter K. Jimack},
    journal={In: Computational Science - ICCS 2024. Lecture Notes in Computer
  Science, vol 14834. Springer, Cham (2024)},
    year={2024},
    doi={10.1007/978-3-031-63759-9_36},
    archivePrefix={arXiv},
    eprint={2404.12282},
    primaryClass={cs.LG}
}