Explore the advancements in Sparse Identification of Nonlinear Dynamics (SINDy) in this 24-minute video lecture by Steve Brunton. Delve into how machine learning is revolutionizing the discovery of dynamical systems models and governing equations from measurement data alone. Examine the SINDy algorithm and its evolution five years after the original paper, focusing on the main challenges in computing sparse nonlinear models from data. Learn about the application of SINDy to partial differential equations and gain insights from Brunton's collaborative work with Nathan Kutz. Access additional resources, including the original SINDy paper and related scientific publications, to deepen your understanding of this cutting-edge approach in sparse machine learning models.
Sparse Identification of Nonlinear Dynamics - Sparse Machine Learning Models 5 Years Later
Steve Brunton via YouTube
Overview
Syllabus
Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
Taught by
Steve Brunton
Related Courses
-
Sparse Identification of Nonlinear Dynamics - SINDy
-
Machine Learning for Fluid Dynamics: Models and Control
-
Data-Driven Dynamical Systems with Machine Learning
-
Sparse Nonlinear Models for Fluid Dynamics with Machine Learning and Optimization
-
Dynamical Systems
-
Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
