Bayesian Calibration of imperfect computer models using Physics-informed priors. (arXiv:2201.06463v2 [stat.ML] UPDATED)

We introduce a computational efficient data-driven framework suitable for
quantifying the uncertainty in physical parameters and model formulation of
computer models, represented by differential equations. We construct
physics-informed priors, which are multi-output GP priors that encode the
model's structure in the covariance function. We extend this into a fully
Bayesian framework that quantifies the uncertainty of physical parameters and
model predictions. Since physical models often are imperfect descriptions of
the real process, we allow the model to deviate from the …

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