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Solving partial differential equations (PDE) is an indispensable part of many branches of science as many processes can be modelled in terms of PDEs.
Journal of Computational Physics
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Conditional computation in neural networks for faster models, 2015
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Physics Informed Deep Learning ( Part I ): Data-driven Solutions of Nonlinear Partial Differential Equations
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Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
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Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks
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On the Convergence and generalization of Physics Informed Neural Networks
Yeonjong Shin, Jerome Darbon, and George Em Karniadakis · 2020
Closest in time.
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Yuyao Chen, Lu Lu, George Em Karniadakis, and Luca Dal Negro · 2020
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