Individual skill
sindy-identification
Sparse Identification of Nonlinear Dynamics (SINDy) — discover governing equations from time-series data. Builds sparse dynamical system models dx/dt = f(x) from measurements using PySINDy. Use when you have trajectory data and want to find the underlying ODE.
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# SINDy — Sparse Identification of Nonlinear Dynamics
## Overview
Discover governing ODEs from time-series data using SINDy (Brunton et al., PNAS 2016). Given measurements of state variables x(t), SINDy identifies the sparse set of terms in a library of candidate functions that best describe dx/dt.
## When to Use
- You have trajectory data and want to find the governing ODE
- System is expected to have a sparse representation (few active terms)
- Input variables are known (you measured the right state variables)
- Data is relatively clean (or you can denoise it)
## Do NOT Use When