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MAGI软件包，用于动态系统的推断。该软件包专注于通过含未知参数的非线性常微分方程建模的动力学过程。此类模型在科学与工程领域广泛应用，但可用于参数估计的观测数据往往存在噪声且稀疏性显著。此外，部分系统分量可能完全不可观测。MAGI在贝叶斯统计框架下，借助流形约束高斯过程解决这一推断难题，而现有软件在处理不可观测分量时面临重大挑战。我们通过多个真实场景的实例展示MAGI的功能。用户可在R、MATLAB及Python环境中自由选择使用该软件包。