Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems
Abstract
We consider physics-informed neural networks (PINNs) (Raissiet al., 2019) for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via Gaussian processes-based Bayesian optimization. We apply the HPO to Helmholtz equation for bounded domains and conduct a thorough study, focusing on: (i) performance, (ii) the collocation points density r and (iii) the frequency kappa, confirming the applicability and necessity of the method. Numerical experiments are performed in two and three dimensions, including comparison to finite element methods.
Más información
Título según WOS: | Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems |
Título según SCOPUS: | ID SCOPUS_ID:85173236600 Not found in local SCOPUS DB |
Título de la Revista: | NEUROCOMPUTING |
Volumen: | 561 |
Editorial: | Elsevier |
Fecha de publicación: | 2023 |
DOI: |
10.1016/J.NEUCOM.2023.126826 |
Notas: | ISI, SCOPUS |