Operator approximation of the wave equation based on deep learning of Green's function
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Publication:6202600
DOI10.1016/j.camwa.2024.01.018arXiv2307.13902OpenAlexW4386301215WikidataQ128285600 ScholiaQ128285600MaRDI QIDQ6202600
Ziad Aldirany, Marc Laforest, Régis Cottereau, Serge Prudhomme
Publication date: 26 March 2024
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2307.13902
wave equationGreen's functionfundamental solutiondeep learningphysics-informed neural networksdeep operator networks
Cites Work
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- Weak adversarial networks for high-dimensional partial differential equations
- Spectral element method for acoustic wave simulation in heterogeneous media
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- DGM: a deep learning algorithm for solving partial differential equations
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
- Physics-informed neural networks for the shallow-water equations on the sphere
- Optimal error analysis of the spectral element method for the 2D homogeneous wave equation
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Reducing the Dimensionality of Data with Neural Networks
- Green’s Functions with Applications, Second Edition
- Learning representations by back-propagating errors
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