Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems

@article{Linka2022BayesianPN,
  title={Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems},
  author={Kevin Linka and Amelie Schafer and Xuhui Meng and Zongren Zou and George Em Karniadakis and Ellen Kuhl},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.08304}
}
Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws… 

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