Numerical approximation of partial differential equations by a variable projection method with artificial neural networks
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Publication:2160472
DOI10.1016/j.cma.2022.115284OpenAlexW4221156459WikidataQ114196757 ScholiaQ114196757MaRDI QIDQ2160472
Publication date: 3 August 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2201.09989
artificial neural networksnonlinear least squaresdeep learninglinear least squaresvariable projectionscientific machine learning
Artificial neural networks and deep learning (68T07) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)
Related Items (4)
Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms ⋮ Physically informed deep homogenization neural network for unidirectional multiphase/multi-inclusion thermoconductive composites ⋮ A method for computing inverse parametric PDE problems with random-weight neural networks ⋮ Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine
Uses Software
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