WeakIdent: Weak formulation for Identifying Differential Equations using Narrow-fit and Trimming

TL;DR

WeakIdent introduces a robust weak formulation approach with narrow-fit and trimming mechanisms to accurately identify differential equations from noisy data, outperforming existing methods in noise resilience.

Contribution

The paper proposes a novel weak formulation framework with narrow-fit and trimming techniques for robust differential equation identification under high noise levels.

Findings

Successfully recovers coefficients with up to 100% noise-to-signal ratio.

Outperforms state-of-the-art algorithms in noisy data scenarios.

Provides a denoising effect while identifying differential equations.

Abstract

Data-driven identification of differential equations is an interesting but challenging problem, especially when the given data are corrupted by noise. When the governing differential equation is a linear combination of various differential terms, the identification problem can be formulated as solving a linear system, with the feature matrix consisting of linear and nonlinear terms multiplied by a coefficient vector. This product is equal to the time derivative term, and thus generates dynamical behaviors. The goal is to identify the correct terms that form the equation to capture the dynamics of the given data. We propose a general and robust framework to recover differential equations using a weak formulation, for both ordinary and partial differential equations (ODEs and PDEs). The weak formulation facilitates an efficient and robust way to handle noise. For a robust recovery against noise and the choice of hyper-parameters, we introduce two new mechanisms, narrow-fit and trimming, for the coefficient support and value recovery, respectively. For each sparsity level, Subspace Pursuit is utilized to find an initial set of support from the large dictionary. Then, we focus on highly dynamic regions (rows of the feature matrix), and error normalize the feature matrix in the narrow-fit step. The support is further updated via trimming of the terms that contribute the least. Finally, the support set of features with the smallest Cross-Validation error is chosen as the result. A comprehensive set of numerical experiments are presented for both systems of ODEs and PDEs with various noise levels. The proposed method gives a robust recovery of the coefficients, and a significant denoising effect which can handle up to $100\%$ noise-to-signal ratio for some equations. We compare the proposed method with several state-of-the-art algorithms for the recovery of differential equations.