CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method
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Publication:2142144
DOI10.1016/j.cma.2022.114909OpenAlexW3208953217MaRDI QIDQ2142144
Yew-Soon Ong, Pao-Hsiung Chiu, Chinchun Ooi, Jian Cheng Wong, My Ha Dao
Publication date: 27 May 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2110.15832
Navier-Stokes equationsinverse problemTaylor series expansionsphysics-informed neural networkcoupled-automatic-numerical differentiationtraining loss formulation
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 (13)
Multi-Fidelity Machine Learning Applied to Steady Fluid Flows ⋮ Scientific machine learning through physics-informed neural networks: where we are and what's next ⋮ VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient ⋮ A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads ⋮ Fully probabilistic deep models for forward and inverse problems in parametric PDEs ⋮ Deep Ritz method with adaptive quadrature for linear elasticity ⋮ Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations ⋮ Adaptive task decomposition physics-informed neural networks ⋮ Solving seepage equation using physics-informed residual network without labeled data ⋮ Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios ⋮ Loss-attentional physics-informed neural networks ⋮ A conservative hybrid deep learning method for Maxwell-Ampère-Nernst-Planck equations ⋮ Stochastic projection based approach for gradient free physics informed learning
Uses Software
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