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Physics-informed neural networks (PINNs) are capable of finding the solution for a given boundary value problem.
Neural algorithm for solving differential equations
Hyuk Lee and In Seok Kang · 1990
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A hybrid neural network-first principles approach to process modeling
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Adadelta: an adaptive learning rate method
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Analysis of different activation functions using back propagation neural networks
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Xue Ying · 2019
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Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior
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Inverse-designed spinodoid metamaterials
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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
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Physics-informed deep learning for computational elastodynamics without labeled data
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Physics-informed deep learning for digital materials
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Bayesian neural networks for weak solution of pdes with uncertainty quantification, 2021
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Locking-free interface failure modeling by a cohesive discontinuous galerkin method for matching and nonmatching meshes
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