Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems

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 equatio...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2023-12, Vol.561, p.126826, Article 126826
Main Authors: Escapil-Inchauspé, Paul, Ruz, Gonzalo A.
Format: Article
Language:English
Subjects:
Bayesian optimization
Helmholtz equation
Hyper-parameter optimization
Physics-informed neural networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 κ, confirming the applicability and necessity of the method. Numerical experiments are performed in two and three dimensions, including comparison to finite element methods.
ISSN:0925-2312
DOI:10.1016/j.neucom.2023.126826