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arXiv · 2604.20141

Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification

Zhiheng Chen·Urban Fasel·Anastasia Bizyaeva
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Abstract

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 effectiveness of this approach in numerical experiments across multiple chaotic and hyperchaotic ODE benchmarks.

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Zhiheng Chen, Urban Fasel, Anastasia Bizyaeva. 2026-07-27. Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification. https://arxiv.org/abs/2604.20141

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