Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification
We introduce Fourier Weak SINDy, a minimal noise-robust and interpretable derivative-free equation learning method that combines weak-form sparse equation learning with spectral density estimation for data-driven test function selection. By using orthogonal sinusoidal test functions inspired by their prevalence in Modulating Function-based system identification, the weak-form sparse regression problem reduces to a regression over Fourier coefficients. Dominant frequencies are then selected via multitaper estimation of the frequency spectrum of the data. This formulation unifies weak-form learning and spectral estimation within a compact and flexible framework. We illustrate the effectiven...
| Authors | Zhiheng Chen, Urban Fasel, Anastasia Bizyaeva |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 27 July 2026 |
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