A Framework for Machine Learning of Model Error in Dynamical Systems. (arXiv:2107.06658v3 [math.DS] UPDATED)

The development of data-informed predictive models for dynamical systems is
of widespread interest in many disciplines. We present a unifying framework for
blending mechanistic and machine-learning approaches to identify dynamical
systems from noisily and partially observed data. We compare pure data-driven
learning with hybrid models which incorporate imperfect domain knowledge. Our
formulation is agnostic to the chosen machine learning model, is presented in
both continuous- and discrete-time settings, and is compatible both with model
errors that exhibit substantial memory and …

arxiv error framework learning machine machine learning math systems