Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

@article{Raissi2019PhysicsinformedNN,
  title={Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations},
  author={M. Raissi and P. Perdikaris and G. Karniadakis},
  journal={J. Comput. Phys.},
  year={2019},
  volume={378},
  pages={686-707}
}
Abstract We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct… Expand
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