Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems-Reference-Cited by-同舟云学术

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

Published:2023-12 Issue: Volume:561 Page:126826
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Escapil-Inchauspé Paul^ORCID,Ruz Gonzalo A.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference32 articles.

1. Acoustic and Electromagnetic Equations: Integral Representations for Harmonic Problems, Vol. 144;Nédélec,2001

2. Numerical Approximation Methods for Elliptic Boundary Value Problems: Finite and Boundary Elements;Steinbach,2007

3. Theory and Practice of Finite Elements;Ern,2004

4. Numerical-asymptotic boundary integral methods in high-frequency acoustic scattering;Chandler-Wilde;Acta Numer.,2012

5. Boundary Element Methods;Sauter,2010

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1. Binary structured physics-informed neural networks for solving equations with rapidly changing solutions;Journal of Computational Physics;2024-12

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4. Discovering physics-informed neural networks model for solving partial differential equations through evolutionary computation;Swarm and Evolutionary Computation;2024-07

5. Optimizing a Physics-Informed Machine Learning Model for Pulsatile Shear-Thinning Channel Flow;Nihon Reoroji Gakkaishi;2024-04-15

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