Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems. (arXiv:2205.08304v2 [cs.LG] UPDATED)

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 of physics and often fail to make
plausible predictions. Here we integrate data, physics, and …

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