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Machine Learning for Fluid Mechanics
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Classroom Contents
Fluid Dynamics
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- 1 Machine Learning for Fluid Mechanics
- 2 Machine Learning for Fluid Dynamics: Patterns
- 3 Machine Learning for Fluid Dynamics: Models and Control
- 4 What Is Turbulence? Turbulent Fluid Dynamics are Everywhere
- 5 Turbulence is Everywhere! Examples of Turbulence and Canonical Flows
- 6 Turbulence: Reynolds Averaged Navier-Stokes (Part 1, Mass Continuity Equation)
- 7 Turbulence: Reynolds Averaged Navier Stokes (RANS) Equations (Part 2, Momentum Equation)
- 8 Turbulence Closure Models: Reynolds Averaged Navier Stokes (RANS) & Large Eddy Simulations (LES)
- 9 Deep Learning for Turbulence Closure Modeling
- 10 Deep Reinforcement Learning for Fluid Dynamics and Control
- 11 Robust Principal Component Analysis (RPCA)
- 12 Robust Modal Decompositions for Fluid Flows
- 13 Data-driven nonlinear aeroelastic models of morphing wings for control
- 14 Data-driven Modeling of Traveling Waves
- 15 Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows
- 16 Modeling synchronization in turbulent flows
- 17 Data-Driven Resolvent Analysis
