Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data
Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems
using state measurements. One approach, known as Sparse Identification of Nonlinear …
using state measurements. One approach, known as Sparse Identification of Nonlinear …
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data
The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the
discovery of parsimonious dynamic models and governing equations from time-series data …
discovery of parsimonious dynamic models and governing equations from time-series data …
PySINDy: A comprehensive Python package for robust sparse system identification
AA Kaptanoglu, BM de Silva, U Fasel… - arXiv preprint arXiv …, 2021 - arxiv.org
Automated data-driven modeling, the process of directly discovering the governing
equations of a system from data, is increasingly being used across the scientific community …
equations of a system from data, is increasingly being used across the scientific community …
Sparse dynamical system identification with simultaneous structural parameters and initial condition estimation
B Wei - Chaos, Solitons & Fractals, 2022 - Elsevier
Abstract Sparse Identification of Nonlinear Dynamics (SINDy) has been shown to
successfully recover governing equations from data; however, this approach assumes the …
successfully recover governing equations from data; however, this approach assumes the …
A toolkit for data-driven discovery of governing equations in high-noise regimes
CB Delahunt, JN Kutz - IEEE Access, 2022 - ieeexplore.ieee.org
We consider the data-driven discovery of governing equations from time-series data in the
limit of high noise. The algorithms developed describe an extensive toolkit of methods for …
limit of high noise. The algorithms developed describe an extensive toolkit of methods for …
Weak SINDy for partial differential equations
DA Messenger, DM Bortz - Journal of Computational Physics, 2021 - Elsevier
Abstract Sparse Identification of Nonlinear Dynamics (SINDy) is a method of system
discovery that has been shown to successfully recover governing dynamical systems from …
discovery that has been shown to successfully recover governing dynamical systems from …
Sparse identification of nonlinear dynamics (sindy)
This work develops a general new framework to discover the governing equations
underlying a dynamical system simply from data measurements, leveraging advances in …
underlying a dynamical system simply from data measurements, leveraging advances in …
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
Accurately modelling the nonlinear dynamics of a system from measurement data is a
challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm …
challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm …
A robust SINDy approach by combining neural networks and an integral form
The discovery of governing equations from data has been an active field of research for
decades. One widely used methodology for this purpose is sparse regression for nonlinear …
decades. One widely used methodology for this purpose is sparse regression for nonlinear …
A priori denoising strategies for sparse identification of nonlinear dynamical systems: A comparative study
In recent years, identification of nonlinear dynamical systems from data has become
increasingly popular. Sparse regression approaches, such as sparse identification of …
increasingly popular. Sparse regression approaches, such as sparse identification of …