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Author (up) Escapil-Inchauspé, P.; Ruz, G.A. doi  openurl
  Title Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems Type
  Year 2023 Publication Neurocomputing Abbreviated Journal Neurocomputing  
  Volume 561 Issue Pages 126826  
  Keywords Physics-informed neural networks; Hyper-parameter optimization; Bayesian optimization; Helmholtz equation  
  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.  
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  ISSN 0925-2312 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001104342800001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1912  
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