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This article presents the MAGI software package for the inference of dynamicsystems. The focus of MAGI is on dynamics modeled by nonlinear ordinarydifferential equations with unknown parameters. While such models are widelyused in science and engineering, the available experimental data for parameterestimation may be noisy and sparse. Furthermore, some system components may beentirely unobserved. MAGI solves this inference problem with the help ofmanifold-constrained Gaussian processes within a Bayesian statisticalframework, whereas unobserved components have posed a significant challenge forexisting software. We use several realistic examples to illustrate thefunctionality of MAGI. The user may choose to use the package in any of the R,MATLAB, and Python environments.

MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes