Sparse dynamical system identification with simultaneous structural parameters and initial condition estimation

Sparse Identification of Nonlinear Dynamics (SINDy) has been shown to successfully recover governing equations from data; however, this approach assumes the initial condition to be exactly known in advance and is sensitive to noise. In this work we propose an integral SINDy (ISINDy) method to simultaneously identify model structure and parameters of nonlinear ordinary differential equations (ODEs) from noisy time -series observations. First, the states are estimated via penalized spline smoothing and then substituted into the integral-form numerical discretization solver, leading to a sparse pseudo linear regression. Then, the sequential threshold least squares is performed to extract the fewest active terms from the overdetermined set of candidate features, thereby estimating structural parameters and initial condition simultaneously and meanwhile, making the identified dynamics parsimonious and interpretable. Simulations detail the method's recovery accuracy and robustness to noise. Examples include a logistic equation, Lotka-Volterra system, and Lorenz system.

Automated summary

The study proposes an integral SINDy (ISINDy) method to identify the model structure and parameters of nonlinear ordinary differential equations (ODEs) from noisy time-series observations. The approach combines penalized spline smoothing and sequential threshold least squares to achieve sparse pseudo linear regression and extract the fewest active terms. The method shows accuracy and robustness to noise in various simulations.