ddmo: A Compatible PySINDy Engine for Feature Optimization in Sparse Dynamical-System Discovery
Year: 2026
Type: preprint
Abstract: Sparse equation discovery depends on the candidate functions, the treatment of noisy data, and the sparse regression procedure. These components are commonly configured independently, although their interactions directly affect model recovery. Our data-driven model optimizer ddmo uses PySINDy as its default sparse-regression backend and adds a modular, multiobjective optimization layer around it. Within one configurable search, users can vary parameters of prescribed candidate functions, regression settings, and the test functions and windows used to construct weak-form equations. For each configuration, the package builds the regression problem, estimates model coefficients with the selected backend, and evaluates the resulting model against objectives that include predictive accuracy and sparsity. This workflow makes it possible to explore how these modeling choices interact without writing a separate optimization pipeline for each study. We demonstrate the framework using the nonlinear Goodwin oscillator under increasing levels of measurement noise. The optimized weak formulation improves noise robustness relative to the strong formulation and supports accurate recovery of sparse dynamics. Joint library optimization explores competing nonlinear function families and estimates their parameters. The package provides a unified optimization layer for investigating how library design, weak formulation, and regularization jointly affect sparse equation discovery.
Source: ChemRxiv
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221781436