This article presents the MAGI software package for the inference of dynamic systems. The focus of MAGI is on dynamics modeled by nonlinear ordinary differential equations with unknown parameters. While such models are widely used in science and engineering, the available experimental data for parameter estimation may be noisy and sparse. Furthermore, some system components may be entirely unobserved. MAGI solves this inference problem with the help of manifold-constrained Gaussian processes within a Bayesian statistical framework, whereas unobserved components have posed a significant challenge for existing software. We use several realistic examples to illustrate the functionality of MAGI. The user may choose to use the package in any of the R, MATLAB, and Python environments.

翻译：本文介绍用于动态系统推断的MAGI软件包。MAGI主要针对由含未知参数的非线性常微分方程刻画的动力学系统。尽管此类模型在科学和工程领域应用广泛，但可用于参数估计的实验数据可能存在噪声大、采样稀疏的问题，且某些系统分量可能完全无法观测。MAGI通过贝叶斯统计框架下基于流形约束的高斯过程解决该推断问题，而不可观测分量一直是现有软件面临的重要挑战。我们通过多个实际案例展示MAGI的功能。用户可在R、MATLAB和Python环境中灵活选用该软件包。