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

Yuzheng Zhang; Rui Carvalho

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arXiv · 2610.34458

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

Yuzheng Zhang·Rui Carvalho

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Abstract

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.

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BibTeX RIS

Yuzheng Zhang, Rui Carvalho. 2026-10-25. py-ARGOS: A Python package for automatic, statistically principled discovery of ordinary differential equations from data. https://arxiv.org/abs/2610.34458

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