Steve Brunton (Eigensteve)

Steve Brunton (Eigensteve)

A collection of videos by Dr. Steve Brunton (Eigensteve) covering topics in reinforcement learning, data-driven fluid dynamics and physics informed machine learning.

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Prof. Peter Vincent
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Dr. Francois Rioual
Dr. Matteo Giacomini
Dr. Shaowu Pan
Prof. Alberto Guardone
Prof Yufei Zhang
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Recent Seminars

University of Southern California

Mode Sensitivity for Fluid Flows via Lagrangian Coherent Structures

Morgan R. Jones, University of Southern California
University of Washington

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Nicholas Zolman, University of Washington
University of Washington

AI/ML+Physics: Recap and Summary

Steve Brunton, University of Washington
California Institute of Technology

Using sparse trajectory data to find Lagrangian Coherent Structures (LCS) in fluid flows

Tanner Harms, California Institute of Technology
University of Washington

AI/ML+Physics Part 5: Employing an Optimization Algorithm

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 4: Crafting a Loss Function

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 3: Designing an Architecture

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 2: Curating Training Data

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 1: Choosing what to model

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics: Preview of Upcoming Modules and Bootcamps

Steve Brunton, University of Washington

Most Watched Seminars

University of Washington

Turbulence Closure Models: Reynolds Averaged Navier Stokes (RANS) & Large Eddy Simulations (LES)

Steven L. Brunton, University of Washington
University of Washington

Overview of Methods

Steven L. Brunton, University of Washington
University of Washington

What Is Turbulence? Turbulent Fluid Dynamics are Everywhere

Steven L. Brunton, University of Washington
University of Washington

Machine Learning for Computational Fluid Dynamics

Steven L. Brunton, University of Washington
University of Washington

Overview of Deep Reinforcement Learning Methods

Steven L. Brunton, University of Washington
University of Washington

Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning

Steven L. Brunton, University of Washington
University of Washington

Lagrangian Coherent Structures (LCS) in unsteady fluids with Finite Time Lyapunov Exponents (FTLE)

Steven L. Brunton, University of Washington
University of Washington

Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning

Steven L. Brunton, University of Washington
University of Washington

Machine Learning for Fluid Mechanics

Steven L. Brunton, University of Washington
University of Washington

Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming

Steven L. Brunton, University of Washington