论文标题
MAGI：通过歧管受限的高斯过程从嘈杂和稀疏数据推理动态系统的软件包
MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes
论文作者
论文摘要
本文介绍了用于推断动态系统的MAGI软件包。 MAGI的重点是由具有未知参数的非线性普通微分方程建模的动力学。尽管此类模型被广泛用于科学和工程中，但可用的参数估计的实验数据可能嘈杂且稀疏。此外，某些系统组件可能完全没有观察到。 Magi借助于贝叶斯统计框架内的多种受限的高斯流程来解决此推论问题，而未观察到的组件对现有软件构成了重大挑战。我们使用几个现实的示例来说明MAGI的功能。用户可以选择在任何R，MATLAB和PYTHON环境中使用软件包。
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.
