A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics
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Publication:6044222
DOI10.1007/s00466-022-02252-0zbMath1521.74404MaRDI QIDQ6044222
Yuantong T. Gu, Laith Alzubaidi, Jinshuai Bai, Ashish Gupta, Timon Rabczuk
Publication date: 17 May 2023
Published in: Computational Mechanics (Search for Journal in Brave)
Full work available at URL: https://eprints.qut.edu.au/236680/1/Revised_Manuscript.pdf
geometric nonlinearitycollocation loss functionenergy loss functionleast squares weighted residual loss function
Artificial neural networks and deep learning (68T07) Numerical and other methods in solid mechanics (74S99)
Related Items (7)
On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures ⋮ A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads ⋮ Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations ⋮ Deep convolutional Ritz method: parametric PDE surrogates without labeled data ⋮ Physics-informed neural network frameworks for crack simulation based on minimized peridynamic potential energy ⋮ Optimal parameters selection of back propagation algorithm in the feedforward neural network ⋮ A complete physics-informed neural network-based framework for structural topology optimization
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