IDENT: Identifying Differential Equations with Numerical Time Evolution

Data driven governing equations approximation using deep neural networks

(2019) Tong Qin et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Hidden physics models: Machine learning of nonlinear partial differential equations

(2018) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Constrained sparse Galerkin regression

(2018) Jean-Christophe Loiseau et al.   JOURNAL OF FLUID MECHANICS

Robust data-driven discovery of governing physical laws with error bars

(2018) Sheng Zhang et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

(2018) E. Kaiser et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Deep learning for universal linear embeddings of nonlinear dynamics

(2018) Bethany Lusch et al.   Nature Communications

Inferring interaction rules from observations of evolutive systems I: The variational approach

(2017) Mattia Bongini et al.   MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES

Exact Recovery of Chaotic Systems from Highly Corrupted Data

(2017) Giang Tran et al.   MULTISCALE MODELING & SIMULATION

Model selection for dynamical systems via sparse regression and information criteria

(2017) N. M. Mangan et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Sparse dynamics for partial differential equations

(2013) H. Schaeffer et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Coherence Pattern–Guided Compressive Sensing with Unresolved Grids

(2012) Albert Fannjiang et al.   SIAM Journal on Imaging Sciences

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE