Top Videos
Top Searches
Moods & Genres
Autumn
Chill
Commute
Energize
Feel-good
Focus
Gaming
Party
Romance
Sad
Sleep
Summer
Winter
Workout
African
Arabic
Blues
Bollywood & Indian
Classical
Country and Americana
Dance and electronic
Decades
Family
Folk and acoustic
German hip-hop
German pop
Hip-hop
Indie and alternative
J-Pop
Jazz
K-Pop
Latin
Mandopop & Cantopop
Metal
Pop
R&B and soul
Reggae and caribbean
Rock
Schlager
Soundtracks and musicals
Search music and videos
Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control
22:28
|
106,397 views
|
Download MP3
Related Videos
A Machine Learning Primer: How to Build an ML Model
Activation Functions in Machine Learning
Probability and Statistics: Overview
Topics in Dynamical Systems: Fixed Points, Linearization, Invariant Manifolds, Bifurcations & Chaos
Convex Sets
The Anatomy of an Optimization Problem
Singular Value Decomposition (SVD): Mathematical Overview
Dual Space of Supporting Hyperplanes
The Divergence of a Vector Field: Sources and Sinks
Convexity 101 [Optimization Bootcamp]
The Anatomy of a Dynamical System
Dynamic Mode Decomposition (Overview)
Monte Carlo Sampling and Bootstrapping in Bayesian Inference
Convexity Implies that Local Minima are Global Minima
The Binomial Distribution and the Multinomial Distribution
Linear Regression
Koopman Spectral Analysis (Overview)
Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering
Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]
Fourier Neural Operator (FNO) [Physics Informed Machine Learning]
Laplace Transforms and Differential Equations
Controllability [Control Bootcamp]
Compressed Sensing: Overview
ME564 Lecture 1: Overview of engineering mathematics
Sparsity and the L1 Norm
Control Bootcamp: Kalman Filter Example in Matlab
Supervised & Unsupervised Machine Learning
Control Bootcamp: Overview
A Neural Network Primer
Machine Learning For Fluid Mechanics - Steven Brunton | Podcast #50
The Kalman Filter [Control Bootcamp]
Steve Brunton - Discovering interpretable and generalizable dynamical systems from data
Conditional Probabilities
Prof. Steve Brunton | Masterclass: data-driven Koopman/DMD methods I
Introduction to Statistics and Data Analysis
Digital Twins
Turbulence: Reynolds Averaged Navier-Stokes (Part 1, Mass Continuity Equation)
Singular Value Decomposition (SVD): Overview
Gauss's Divergence Theorem
Norms are Convex
Taylor Series and Power Series Made Easy (with Pictures): Review of Calculus
Bayesian Inference: Overview
New Book!!! Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Why images are compressible: The Vastness of Image Space
Neural ODEs (NODEs) [Physics Informed Machine Learning]
Python Symbolic Regression (PySR) [Physics Informed Machine Learning]
Steve Brunton: "Dynamical Systems (Part 1/2)"
Inverted Pendulum on a Cart [Control Bootcamp]
Runge-Kutta Integrator Overview: All Purpose Numerical Integration of Differential Equations
System Identification: Dynamic Mode Decomposition with Control
The Moment Generating Function
Control Bootcamp: Observability
Are all vector fields the gradient of a potential? ... and the Helmholtz Decomposition
The Fast Fourier Transform Algorithm
Maximum Likelihood Estimation (MLE) with Examples
Model Predictive Control
Controllability, Reachability, and Eigenvalue Placement [Control Bootcamp]
Exponential Growth and Euler
Turbulence Closure Models: Reynolds Averaged Navier Stokes (RANS) & Large Eddy Simulations (LES)
Differential Equations and Dynamical Systems: Overview
Bayesian Linear Regression and Maximum Likelihood Estimates
Convex Functions
Principal Component Analysis (PCA)
Deep Reinforcement Learning for Fluid Dynamics and Control
Deep Operator Networks (DeepONet) [Physics Informed Machine Learning]
Control Bootcamp: Full-State Estimation
Types of Machine Learning 2
Koopman Observable Subspaces & Finite Linear Representations of Nonlinear Dynamics for Control
Affine Transformations and Convexity
Machine Learning for Fluid Mechanics
Extremum Seeking Control
Consistency of Parameter Estimates in Statistics
PDE 101: Separation of Variables! ...or how I learned to stop worrying and solve Laplace's equation
Laplace's Equation and Potential Flow
Control Bootcamp: Three Equivalent Representations of Linear Systems
The Discrete Fourier Transform (DFT)
Methods for System Identification (Prof. Steve L. Brunton) – Part 2
Intro to Data Science: What is Data Science?
Calculus Review: The Derivative (and the Power Law and Chain Rule)
Compressed Sensing: Mathematical Formulation
Randomized SVD: Power Iterations and Oversampling
The Normal Distribution: The Limit of Binomial Distribution for Large "n"
Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming
Neural Implicit Flow (NIF) [Physics Informed Machine Learning]
Neural Network Architectures & Deep Learning
What is Sparsity?
Steve Brunton: "Discovering interpretable and generalizable dynamical systems from data"
Gentle Introduction to Modeling with Matrices and Vectors: A Probabilistic Weather Model
Linear Regression 1 [Python]
Control Bootcamp: Limitations on Robustness
Fourier Series: Part 1
Shannon Nyquist Sampling Theorem
Bayes' Theorem (with Example!)
Pole Placement for the Inverted Pendulum on a Cart [Control Bootcamp]
Reinforcement Learning: Machine Learning Meets Control Theory
Data-Driven Control: The Goal of Balanced Model Reduction
The Hartman-Grobman Theorem, Structural Stability of Linearization, and Stable/Unstable Manifolds
Method of Moments to Fit Distributions from Data
Independence in Probability
Control Bootcamp: Observability Example in Matlab
Numerical Simulation of Ordinary Differential Equations: Integrating ODEs
The Simplest Ordinary Differential Equation (ODE) and Its Exponential Solution
Numerical Differentiation with Finite Difference Derivatives
Intro to Data Science: The Nature of Data
Linear Systems of Equations, Least Squares Regression, Pseudoinverse
Properties of Convex Functions
Steve Brunton: "Introduction to Fluid Mechanics"
Machine Learning Overview
Interpretable and Generalizable Machine Learning for Fluid Dynamics ▸ Steven Brunton #CLIMATE-C21
Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning
Parseval's Theorem
Deriving the Wave Equation
System Identification: Koopman with Control
Intro to Data Science: Overview
Matrix Systems of Differential Equations
ME564 Lecture 8: 2x2 systems of ODEs (with eigenvalues and eigenvectors), phase portraits
Applications of Optimization
The Fast Fourier Transform (FFT)
Controllability and the PBH Test [Control Bootcamp]
Sparse Nonlinear Dynamics Models with SINDy, Part 2: Training Data & Disambiguating Models
ME564 Lecture 2: Review of calculus and first order linear ODEs
The Laplace Transform: A Generalized Fourier Transform
Control Bootcamp: Laplace Transforms and the Transfer Function
Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming
What Is Turbulence? Turbulent Fluid Dynamics are Everywhere
Discrete-Time Dynamical Systems
Denoising Data with FFT [Python]
Residual Networks (ResNet) [Physics Informed Machine Learning]
Reinforcement Learning Series: Overview of Methods
Bayesian Updates and Conjugate Priors
Data-Driven Control: Eigensystem Realization Algorithm
The Gradient Operator in Vector Calculus: Directions of Fastest Change & the Directional Derivative
Joint Probability Distributions
Robust, Interpretable Statistical Models: Sparse Regression with the LASSO
Maximum Likelihood Estimation Example: Fitting a Normal Distribution with Data
Machine Learning for Fluid Dynamics: Patterns
Linear Systems of Differential Equations with Forcing: Convolution and the Dirac Delta Function
The Future of Model Based Engineering: Collimator 2.0
Hypothesis Testing Revisited: Normal, t, and Chi-Squared Distribution Tests
Population Statistics and Random Sampling
The Central Limit Theorem
Methods for System Identification (Prof. Steve L. Brunton)
Wavelets and Multiresolution Analysis
Properties of Maximum Likelihood Estimation
Quality Control, Non-Destructive Inspection, and the Multinomial Distribution
Div, Grad, and Curl: Vector Calculus Building Blocks for PDEs [Divergence, Gradient, and Curl]
Deep Learning of Dynamics and Coordinates with SINDy Autoencoders
Extremum Seeking Control Applications
Principal Component Analysis (PCA) 2 [Python]
Student's t-distribution in Statistics
AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]
Controllability and the Discrete-Time Impulse Response [Control Bootcamp]
Differential Equations with Forcing: Method of Variation of Parameters
Bayesian Maximum Aposteriori Estimation (MAP): Extending Maximum Likelihood Estimation
Machine Learning and Cross-Validation
Steve Brunton - Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
Simulating the Lorenz System in Matlab
Neural Networks and Deep Learning
Why Navier-Stokes Pushes Math and Physics to the Edge
Control Bootcamp: Sensitivity and Complementary Sensitivity (Part 2)
Introduction to Machine Learning Methods (Prof. Steve L. Brunton) – Part 1
Machine Learning Control: Genetic Algorithms
The Spectrogram and the Gabor Transform
Critical Transitions in Complex Systems - Talk by Prof. Steven Brunton
Underdetermined systems and compressed sensing [Matlab]
Random Variables and Probability Distributions
The Curl of a Vector Field: Measuring Rotation
Introduction to Machine Learning Methods (Prof. Steve L. Brunton) – Part 3
Steve Brunton: Machine Learning for Fluid Dynamics
Steve Brunton: "Machine Learning for Fluid Mechanics"
The Expected Value (Mean) of a Probability Distribution
Image Compression with Wavelets (Examples in Python)
Introduction to Machine Learning Methods (Prof. Steve L. Brunton) – Part 2
Jensen's Inequality
Bernoulli and Binomial Random Variables
Deriving the Heat Equation: A Parabolic Partial Differential Equation for Heat Energy Conservation
AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]
The Standard Unit Normal and Probability Computations
The Law of Large Numbers
Prof. Steve Brunton | Masterclass: data-driven Koopman/DMD methods II
Complex Analysis L07: Analytic Functions Solve Laplace's Equation
Control Bootcamp: Introduction to Robust Control
Chebyshev's Inequality in Probability: Second Order Estimates
Machine Learning in Fluids: Pairing Methods with Problems (Prof. Steve L. Brunton) – Part 3
Control Bootcamp: Cruise Control Example with Proportional-Integral (PI) control
Solving Differential Equations with Power Series: A Simple Example
Machine Learning in Fluids: Pairing Methods with Problems (Prof. Steve L. Brunton) – Part 2
Complex Analysis L13: Bromwich Integrals and the Inverse Laplace Transform
Stability and Eigenvalues [Control Bootcamp]
Linear Systems of Equations, Ax=b
Control Meets Learning Seminar by Steve Brunton (University of Washington) || May 5, 2021
Turbulence: Reynolds Averaged Navier Stokes (RANS) Equations (Part 2, Momentum Equation)
Dynamic Mode Decomposition (Code)
Introduction to Machine Learning Methods (Prof. Steve L. Brunton) – Part 4
Hypothesis Testing Procedure
Introduction to Machine Learning Methods (Prof. Steve L. Brunton)
Partial Differential Equations Overview
The Exponential Distribution: Time Between Poisson Events
Deep Learning for Turbulence Closure Modeling
Solving Systems of Differential Equations with Eigenvalues and Eigenvectors
Steve Brunton: Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
Deep Delay Autoencoders Discover Dynamical Systems w Latent Variables: Deep Learning meets Dynamics!
Confidence Intervals
Motivating Eigenvalues and Eigenvectors with Differential Equations
Linear Quadratic Regulator (LQR) Control for the Inverted Pendulum on a Cart [Control Bootcamp]
Types of Machine Learning 1
Numerical Differentiation: Second Derivatives and Differentiating Data
Robust Principal Component Analysis (RPCA)
Interpretable Deep Learning for New Physics Discovery
Hypothesis Testing in Statistics
Systems of Differential Equations: Diagonalization and Jordan Canonical Form
ME565 Lecture 14: Fourier Transforms
Data-Driven Control: Balanced Proper Orthogonal Decomposition
Set Theory in Probability: Sample Spaces and Events
2016 AIAA AVIATION Forum: Flow Control - Steve Brunton
Engineering Math Pre-Req: Quick and Dirty Introduction to Python
The Chi Squared Distribution: The Square of the Normal Distribution
Bootstrapping and Monte Carlo Sampling in Statistics
Stokes' Theorem and Green's Theorem
Data-Driven Control: Eigensystem Realization Algorithm Procedure
Linear Systems [Control Bootcamp]
Discrepancy Modeling with Physics Informed Machine Learning
Stability and Eigenvalues: What does it mean to be a "stable" eigenvalue?
Unitary Transformations
The Poisson Distribution: The Rare Event Limit of a Binomial Distribution
Engineering Math Pre-Req: Quick and Dirty Introduction to Matlab
Properties of Chi-Squared and Student's t Distributions
Complex Analysis L06: Analytic Functions and Cauchy-Riemann Conditions
Data-Driven Control: ERA/OKID Example in Matlab
Eigenvalues and Eigenvectors
The Birthday Problem in Probability: P(A) = 1 - P(not A)
Lies, Damn Lies, and Statistics... P-Hacking
Principal Component Analysis (PCA) [Matlab]
Cayley-Hamilton Theorem [Control Bootcamp]
Solving the Heat Equation with the Fourier Transform
Uncertainty Principles and the Fourier Transform
Hankel Alternative View of Koopman (HAVOK) Analysis [FULL]
Physical Review Fluids 10-Year Anniversary: Author Reflections Interview Series — 1
Compressed Sensing: When It Works
Parameter Estimation and Fitting Distributions
Nonlinear Dynamical Systems (Prof. Steve L. Brunton) – Part 2
Motivation for Full-State Estimation [Control Bootcamp]
Solving PDEs with the Laplace Transform: The Wave Equation
Hamiltonian Neural Networks (HNN) [Physics Informed Machine Learning]
Control Bootcamp: Example Frequency Response (Bode Plot) for Spring-Mass-Damper
Data-Driven Control: Error Bounds for Balanced Truncation
The Method of Characteristics and the Wave Equation
Sparse Identification of Nonlinear Dynamics for Model Predictive Control
Fourier Series: Part 2
System Identification: Full-State Models with Control
Numerical Integration: Discrete Riemann Integrals and Trapezoid Rule
Robust Regression with the L1 Norm