[HTML][HTML] Sparse identification of nonlinear dynamics for rapid model recovery
Big data have become a critically enabling component of emerging mathematical methods
aimed at the automated discovery of dynamical systems, where first principles modeling may …
aimed at the automated discovery of dynamical systems, where first principles modeling may …
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 …
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 …
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
Sparse model identification enables the discovery of nonlinear dynamical systems purely
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …
[HTML][HTML] 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 …
Pysindy: a python package for the sparse identification of nonlinear dynamics from data
BM de Silva, K Champion, M Quade… - arXiv preprint arXiv …, 2020 - arxiv.org
PySINDy is a Python package for the discovery of governing dynamical systems models
from data. In particular, PySINDy provides tools for applying the sparse identification of …
from data. In particular, PySINDy provides tools for applying the sparse identification of …
AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression
Discovering mathematical models that characterize the observed behavior of dynamical
systems remains a major challenge, especially for systems in a chaotic regime, due to their …
systems remains a major challenge, especially for systems in a chaotic regime, due to their …
Learning discrepancy models from experimental data
First principles modeling of physical systems has led to significant technological advances
across all branches of science. For nonlinear systems, however, small modeling errors can …
across all branches of science. For nonlinear systems, however, small modeling errors can …
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 …