Interpretable and Generalizable Machine Learning for Fluid Dynamics - Steven Brunton

Explore interpretable and generalizable machine learning techniques for fluid dynamics in this conference talk from the Machine Learning for Climate KITP conference. Delve into the challenges of informing society about future climate changes at regional and local scales, and discover how big data and machine learning algorithms can provide unprecedented insights into climate systems. Examine the potential for descriptive inference to enable climate scientists to ask causal questions and validate theories. Learn about the integration of machine learning with modeling experiments and model parameterizations to address complex climate-related questions. Gain insights into sparse identification of nonlinear dynamics, joint loss functions, and the incorporation of physical constraints in machine learning models for fluid dynamics applications.

Introduction
Review Paper
Machine Learning for Fluid Dynamics
Patterns in Data
Example
Machine Learning Philosophy
Sparse Identification of Nonlinear Dynamics
Joint Loss
Constraints