Web: http://arxiv.org/abs/2107.06658

May 12, 2022, 1:10 a.m. | Matthew E. Levine, Andrew M. Stuart

stat.ML updates on arXiv.org arxiv.org

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 framework learning machine machine learning math model systems

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