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 …
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 …
Data-driven sparse identification of nonlinear dynamical systems using linear multistep methods
H Chen - Calcolo, 2023 - Springer
Linear multistep methods (LMMs) are popular time discretization schemes for solving the
forward problem on differential equations. Recently, LMMs together with deep neural …
forward problem on differential equations. Recently, LMMs together with deep neural …
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 …
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 …
Learning sparse nonlinear dynamics via mixed-integer optimization
D Bertsimas, W Gurnee - Nonlinear Dynamics, 2023 - Springer
Discovering governing equations of complex dynamical systems directly from data is a
central problem in scientific machine learning. In recent years, the sparse identification of …
central problem in scientific machine learning. In recent years, the sparse identification of …
Benchmarking sparse system identification with low-dimensional chaos
Sparse system identification is the data-driven process of obtaining parsimonious differential
equations that describe the evolution of a dynamical system, balancing model complexity …
equations that describe the evolution of a dynamical system, balancing model complexity …
Kernel functions embed into the autoencoder to identify the sparse models of nonlinear dynamics
X Dong, YL Bai, WD Wan - … in Nonlinear Science and Numerical Simulation, 2024 - Elsevier
Numerous researches have shown that there are three main challenges in data-driven
model identification methods: high-dimensional measurements, system complexity and …
model identification methods: high-dimensional measurements, system complexity and …
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 …
SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis
Abstract Machine learning methods have revolutionized studies in several areas of
knowledge, helping to understand and extract information from experimental data. Recently …
knowledge, helping to understand and extract information from experimental data. Recently …