Steve Brunton Courses

Stokes' Theorem and Green's Theorem

Explore Stokes' and Green's theorems, their geometric interpretations, and practical applications in mathematical physics, including examples and land area calculations.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

The Divergence of a Vector Field: Sources and Sinks

Explore the divergence operator in vector calculus, understanding its role in quantifying local expansion or contraction of vector fields, with examples and applications in fluid dynamics.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Overview of Deep Reinforcement Learning Methods

Comprehensive exploration of deep reinforcement learning techniques, including Q-learning, actor-critic methods, policy networks, and gradient optimization for advanced AI decision-making and control systems.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

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

Explore Q-learning and temporal difference algorithms in reinforcement learning, including SARSA and connections to biological learning through dopamine. Covers key concepts for deep reinforcement learning.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Model Based Reinforcement Learning - Policy Iteration, Value Iteration, and Dynamic Programming

Explore dynamic programming in model-based reinforcement learning, covering policy iteration, value iteration, and Q-learning for effective decision-making strategies.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Machine Learning for Computational Fluid Dynamics

Explore machine learning's potential to enhance computational fluid dynamics, covering applications in simulations, turbulence modeling, and reduced-order models while emphasizing physics-informed approaches.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

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

Explore deep learning techniques for discovering effective coordinate systems in dynamical systems, enabling simpler models and physical law discovery. Focus on autoencoders and physics-informed machine learning.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Interpretable Deep Learning for New Physics Discovery

Explore a novel method combining deep learning and symbolic regression to uncover physical laws from data, with applications in physics and cosmology.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Deep Learning for Turbulence Closure Modeling

Explore how deep learning revolutionizes turbulence modeling in fluid dynamics, focusing on RANS equations and large eddy simulations for advanced computational techniques.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

Turbulence Closure Models - Reynolds Averaged Navier Stokes and Large Eddy Simulations

Explore turbulence closure models, focusing on RANS and LES approaches for approximating complex fluid dynamics. Learn key concepts and applications in scientific computing.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Turbulence Is Everywhere! - Examples of Turbulence and Canonical Flows

Explore turbulence in fluid dynamics through canonical examples and real-world applications. Discover engineering implications and gain insights into this fascinating phenomenon.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

What Is Turbulence? - Turbulent Fluid Dynamics Are Everywhere

Explore the fundamental characteristics of turbulent fluid dynamics, their prevalence in nature and engineering, and the role of Reynolds number in controlling complexity.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Deep Reinforcement Learning - Neural Networks for Learning Control Laws

Explore neural networks in deep reinforcement learning for control systems, from game-playing AI to robotics. Learn about key algorithms and groundbreaking applications.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers

Learn to integrate multiscale differential equations efficiently using a novel deep learning architecture, with benchmarks on illustrative dynamical systems.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Machine Learning for Fluid Dynamics: Models and Control

Explore machine learning applications in fluid dynamics modeling and control, covering patterns, complexity, RANS closure models, SINDY, and deep MPC for flow control.

• YouTube
• 32 minutes
• Self-Paced
• Free Video