Explore the fascinating world of fluid dynamics through a comprehensive 6-hour video series. Delve into machine learning applications for fluid mechanics, patterns, models, and control. Gain a deep understanding of turbulence, its prevalence in everyday life, and canonical flows. Learn about Reynolds Averaged Navier-Stokes equations and turbulence closure models. Discover cutting-edge techniques like deep learning for turbulence modeling and reinforcement learning for fluid dynamics control. Investigate advanced topics such as robust principal component analysis, modal decompositions, and data-driven modeling of traveling waves and morphing wings. Examine energy-optimal trajectories in unsteady flows, synchronization in turbulent flows, and data-driven resolvent analysis.
Overview
Syllabus
Machine Learning for Fluid Mechanics.
Machine Learning for Fluid Dynamics: Patterns.
Machine Learning for Fluid Dynamics: Models and Control.
What Is Turbulence? Turbulent Fluid Dynamics are Everywhere.
Turbulence is Everywhere! Examples of Turbulence and Canonical Flows.
Turbulence: Reynolds Averaged Navier-Stokes (Part 1, Mass Continuity Equation).
Turbulence: Reynolds Averaged Navier Stokes (RANS) Equations (Part 2, Momentum Equation).
Turbulence Closure Models: Reynolds Averaged Navier Stokes (RANS) & Large Eddy Simulations (LES).
Deep Learning for Turbulence Closure Modeling.
Deep Reinforcement Learning for Fluid Dynamics and Control.
Robust Principal Component Analysis (RPCA).
Robust Modal Decompositions for Fluid Flows.
Data-driven nonlinear aeroelastic models of morphing wings for control.
Data-driven Modeling of Traveling Waves.
Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows.
Modeling synchronization in turbulent flows.
Data-Driven Resolvent Analysis.
Taught by
Steve Brunton
