Methods: Machine Learning
Fields: Computer Science

Project lead: Steven Brunton


Sparse Identification of Nonlinear Dynamics (SINDy) method

There are many critical data-driven problems, such as understanding cognition from neural recordings, inferring climate patterns, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, extracting governing equations from data is a central challenge in many diverse areas of science and engineering.

A recent breakthrough in the Brunton lab has resulted in a novel framework to discover governing equations underlying a dynamical system simply from measurement data, leveraging advances in sparsity techniques and machine learning. We call this the sparse identification of nonlinear dynamics (SINDy) algorithm.

For more information, view the PNAS paper [open access] or watch the video abstract on YouTube.