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Sparse Nonlinear Models for Fluid Dynamics with Machine Learning and Optimization

Steve Brunton via YouTube

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Overview

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This course explores sparse nonlinear machine learning for reduced-order modeling of complex fluid dynamics. It covers SINDy, PDE discovery, autoencoder coordinates, Galerkin regression, stochastic turbulence models, and data-driven dominant-balance analysis.

Syllabus

Introduction.
Interpretable and Generalizable Machine Learning.
SINDy Overview.
Discovering Partial Differential Equations.
Deep Autoencoder Coordinates.
Modeling Fluid Flows with Galerkin Regression.
Chaotic thermo syphon.
Chaotic electroconvection.
Magnetohydrodynamics.
Nonlinear correlations.
Stochastic SINDy models for turbulence.
Dominant balance physics modeling.

Taught by

Steve Brunton

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