arXiv:2610.34458 · v1

py-ARGOS: A Python package for automatic, statistically principled discovery of ordinary differential equations from data

首次提交 2026-10-26 01:25

版本更新 2026-10-26 01:25

雷达观测 2026-10-29 00:36

摘要

Recovering the governing equations of a dynamical system from data is a central problem in data-driven science, yet the available Python tooling forces a compromise among automation, statistical rigor, and reliability. We present py-ARGOS, a Python package implementing two automatic algorithms, bootstrap ARGOS and Bayesian-ARGOS, behind a uniform interface. Both are statistically principled throughout: automated model selection reduces the candidate library, each coefficient is reported with a bootstrap confidence or Bayesian credible interval that quantifies its uncertainty, and a term is retained when this interval excludes zero. Built-in diagnostics additionally flag unreliable fits. On chaotic benchmark systems, the two algorithms improve on the SINDy family's identification performance.

作者备注：17 pages, 4 figures