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 …
Physics-informed learning of governing equations from scarce data
Harnessing data to discover the underlying governing laws or equations that describe the
behavior of complex physical systems can significantly advance our modeling, simulation …
behavior of complex physical systems can significantly advance our modeling, simulation …
Data-driven discovery of coordinates and governing equations
The discovery of governing equations from scientific data has the potential to transform data-
rich fields that lack well-characterized quantitative descriptions. Advances in sparse …
rich fields that lack well-characterized quantitative descriptions. Advances in sparse …
Methods for data-driven multiscale model discovery for materials
SL Brunton, JN Kutz - Journal of Physics: Materials, 2019 - iopscience.iop.org
Despite recent achievements in the design and manufacture of advanced materials, the
contributions from first-principles modeling and simulation have remained limited, especially …
contributions from first-principles modeling and simulation have remained limited, especially …
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 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 …
Robust data-driven discovery of governing physical laws with error bars
Discovering governing physical laws from noisy data is a grand challenge in many science
and engineering research areas. We present a new approach to data-driven discovery of …
and engineering research areas. We present a new approach to data-driven discovery of …
Promoting global stability in data-driven models of quadratic nonlinear dynamics
Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of
reduced-order models for a variety of scientific and engineering tasks. However, it is …
reduced-order models for a variety of scientific and engineering tasks. However, it is …
On the convergence of the SINDy algorithm
L Zhang, H Schaeffer - Multiscale Modeling & Simulation, 2019 - SIAM
One way to understand time-series data is to identify the underlying dynamical system which
generates it. This task can be done by selecting an appropriate model and a set of …
generates it. This task can be done by selecting an appropriate model and a set of …
Sparsifying priors for Bayesian uncertainty quantification in model discovery
SM Hirsh, DA Barajas-Solano… - Royal Society Open …, 2022 - royalsocietypublishing.org
We propose a probabilistic model discovery method for identifying ordinary differential
equations governing the dynamics of observed multivariate data. Our method is based on …
equations governing the dynamics of observed multivariate data. Our method is based on …