magi: 'magi': MAnifold-Constrained Gaussian Process Inference

In magi: MAnifold-Constrained Gaussian Process Inference

magi: MAnifold-Constrained Gaussian Process Inference

Description

magi is a package that provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. In the references below, please see Yang, Wong, and Kou (2021) for details of the MAGI method (MAnifold-constrained Gaussian process Inference), and Wong, Yang, and Kou (2022) for a detailed user guide.

References

Yang, S., Wong, S. W. K., & Kou, S. C. (2021). Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes. Proceedings of the National Academy of Sciences, 118 (15), e2020397118. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.2020397118")}

Wong, S. W. K., Yang, S., & Kou, S. C. (2022). MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes. https://arxiv.org/abs/2203.06066

magi documentation built on April 26, 2023, 1:12 a.m.