Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems

• URL: http://arxiv.org/abs/2509.14219v1
• Date: Wed, 17 Sep 2025 17:51:43 GMT
• Title: Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems
• Authors: Jiaqi Yao, Lewis Mitchell, John Maclean, Hemanth Saratchandran,
• Abstract summary: Data-driven modeling of nonlinear dynamical systems is often hampered by measurement noise.<n>We propose a denoising framework that represents the state trajectory with an implicit neural representation fitted directly to noisy observations.<n>Experiments demonstrate effective noise suppression, precise derivative estimation, and reliable system identification.
• Score: 7.364096151612954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven modeling of nonlinear dynamical systems is often hampered by measurement noise. We propose a denoising framework, called Runge-Kutta and Total Variation Based Implicit Neural Representation (RKTV-INR), that represents the state trajectory with an implicit neural representation (INR) fitted directly to noisy observations. Runge-Kutta integration and total variation are imposed as constraints to ensure that the reconstructed state is a trajectory of a dynamical system that remains close to the original data. The trained INR yields a clean, continuous trajectory and provides accurate first-order derivatives via automatic differentiation. These denoised states and derivatives are then supplied to Sparse Identification of Nonlinear Dynamics (SINDy) to recover the governing equations. Experiments demonstrate effective noise suppression, precise derivative estimation, and reliable system identification.

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