Physics-informed learning of governing equations from scarce data

@article{Chen2020PhysicsinformedLO,
  title={Physics-informed learning of governing equations from scarce data},
  author={Zhao Chen and Yang Liu and Hao Sun},
  journal={Nature Communications},
  year={2020},
  volume={12}
}
  • Zhao ChenYang LiuHao Sun
  • Published 5 May 2020
  • Computer Science
  • Nature Communications
Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel approach called physics-informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this… 

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