Канал: Steve Brunton

The Law of Total Probability

The Law of Total Probability

Conditional Probabilities

Conditional Probabilities

The Binomial Distribution and the Multinomial Distribution

The Binomial Distribution and the Multinomial Distribution

Quality Control, Non-Destructive Inspection, and the Multinomial Distribution

Quality Control, Non-Destructive Inspection, and the Multinomial Distribution

Mode Sensitivity for Fluid Flows via Lagrangian Coherent Structures

Mode Sensitivity for Fluid Flows via Lagrangian Coherent Structures

The Birthday Problem in Probability: P(A) = 1 - P(not A)

The Birthday Problem in Probability: P(A) = 1 - P(not A)

Set Theory in Probability: Sample Spaces and Events

Set Theory in Probability: Sample Spaces and Events

Counting Probabilities with Combinatorics and the Factorial

Counting Probabilities with Combinatorics and the Factorial

Gentle Introduction to Probability: Counting Coin Flips and Dice

Gentle Introduction to Probability: Counting Coin Flips and Dice

Probability and Statistics: Overview

Probability and Statistics: Overview

Python Symbolic Regression (PySR) [Physics Informed Machine Learning]

Python Symbolic Regression (PySR) [Physics Informed Machine Learning]

Neural Implicit Flow (NIF) [Physics Informed Machine Learning]

Neural Implicit Flow (NIF) [Physics Informed Machine Learning]

Fourier Neural Operator (FNO) [Physics Informed Machine Learning]

Fourier Neural Operator (FNO) [Physics Informed Machine Learning]

Deep Operator Networks (DeepONet) [Physics Informed Machine Learning]

Deep Operator Networks (DeepONet) [Physics Informed Machine Learning]

Hamiltonian Neural Networks (HNN) [Physics Informed Machine Learning]

Hamiltonian Neural Networks (HNN) [Physics Informed Machine Learning]

Lagrangian Neural Network (LNN) [Physics Informed Machine Learning]

Lagrangian Neural Network (LNN) [Physics Informed Machine Learning]

Neural Network Control in Collimator 2.0 & New Educational Videos!!!

Neural Network Control in Collimator 2.0 & New Educational Videos!!!

Residual Networks (ResNet) [Physics Informed Machine Learning]

Residual Networks (ResNet) [Physics Informed Machine Learning]

Neural ODEs (NODEs) [Physics Informed Machine Learning]

Neural ODEs (NODEs) [Physics Informed Machine Learning]

Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]

Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]

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

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

AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]

AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]

AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]

AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]

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

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

AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]

AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]

The Future of Model Based Engineering: Collimator 2.0

The Future of Model Based Engineering: Collimator 2.0

Collimator 2.0 and Quanser!!!

Collimator 2.0 and Quanser!!!

AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning]

AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning]

AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]

AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]

AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]

AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]

AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]

AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]

Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering

Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering

Can we make commercial aircraft faster? Mitigating transonic buffet with porous trailing edges

Can we make commercial aircraft faster? Mitigating transonic buffet with porous trailing edges

A Neural Network Primer

A Neural Network Primer

Supervised & Unsupervised Machine Learning

Supervised & Unsupervised Machine Learning

A Machine Learning Primer: How to Build an ML Model

A Machine Learning Primer: How to Build an ML Model

Arousal as a universal embedding for spatiotemporal brain dynamics

Arousal as a universal embedding for spatiotemporal brain dynamics

New Advances in Artificial Intelligence and Machine Learning

New Advances in Artificial Intelligence and Machine Learning

Nonlinear parametric models of viscoelastic fluid flows with SINDy

Nonlinear parametric models of viscoelastic fluid flows with SINDy

[5/8] Control for Societal-Scale Challenges: Road Map 2030 [Technology, Validation, and Transition]

[5/8] Control for Societal-Scale Challenges: Road Map 2030 [Technology, Validation, and Transition]

[8/8] Control for Societal-Scale Challenges: Road Map 2030 [Recommendations]

[8/8] Control for Societal-Scale Challenges: Road Map 2030 [Recommendations]

[2/8] Control for Societal-Scale Challenges: Road Map 2030 [Societal Drivers]

[2/8] Control for Societal-Scale Challenges: Road Map 2030 [Societal Drivers]

[1/8] Control for Societal-Scale Challenges: Road Map 2030 [Introduction]

[1/8] Control for Societal-Scale Challenges: Road Map 2030 [Introduction]

[6/8] Control for Societal-Scale Challenges: Road Map 2030 [Education]

[6/8] Control for Societal-Scale Challenges: Road Map 2030 [Education]

[3/8] Control for Societal-Scale Challenges: Road Map 2030 [Technological Trends]

[3/8] Control for Societal-Scale Challenges: Road Map 2030 [Technological Trends]

[4/8] Control for Societal-Scale Challenges: Road Map 2030 [Emerging Methodologies]

[4/8] Control for Societal-Scale Challenges: Road Map 2030 [Emerging Methodologies]

[7/8] Control for Societal-Scale Challenges: Road Map 2030 [Ethics, Fairness, & Regulatory Issues]

[7/8] Control for Societal-Scale Challenges: Road Map 2030 [Ethics, Fairness, & Regulatory Issues]

🤯🤯🤯 Integrating GPT-4 into Collimator.ai for Symbolic Modeling and Control** 🔥🔥🔥

🤯🤯🤯 Integrating GPT-4 into Collimator.ai for Symbolic Modeling and Control** 🔥🔥🔥

New Book!!! Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

New Book!!! Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

Complex Analysis L13: Bromwich Integrals and the Inverse Laplace Transform

Complex Analysis L13: Bromwich Integrals and the Inverse Laplace Transform

Complex Analysis L12: Examples of Complex Integrals

Complex Analysis L12: Examples of Complex Integrals

Complex analysis 101: imaginary numbers are real!

Complex analysis 101: imaginary numbers are real!

Complex Analysis L11: Examples of Cauchy-Integral Formula

Complex Analysis L11: Examples of Cauchy-Integral Formula

Complex Analysis L10: Cauchy Integral Formula

Complex Analysis L10: Cauchy Integral Formula

Complex Analysis L09: Complex Residues

Complex Analysis L09: Complex Residues

Complex Analysis L08: Integrals in the Complex Plane

Complex Analysis L08: Integrals in the Complex Plane

Complex Analysis L07: Analytic Functions Solve Laplace's Equation

Complex Analysis L07: Analytic Functions Solve Laplace's Equation

Complex Analysis L06: Analytic Functions and Cauchy-Riemann Conditions

Complex Analysis L06: Analytic Functions and Cauchy-Riemann Conditions

Complex Analysis L05: Roots of Unity and Rational Powers of z

Complex Analysis L05: Roots of Unity and Rational Powers of z

Complex Analysis L04: The Complex Logarithm, Log(z)

Complex Analysis L04: The Complex Logarithm, Log(z)

Complex Analysis L03: Functions of a complex variable, f(z)

Complex Analysis L03: Functions of a complex variable, f(z)

Complex Analysis L02: Euler's formula, one of the most important formulas in all of mathematics

Complex Analysis L02: Euler's formula, one of the most important formulas in all of mathematics

Complex Analysis L01: Overview & Motivation, Complex Arithmetic, Euler's Formula & Polar Coordinates

Complex Analysis L01: Overview & Motivation, Complex Arithmetic, Euler's Formula & Polar Coordinates

Topics in Dynamical Systems: Fixed Points, Linearization, Invariant Manifolds, Bifurcations & Chaos

Topics in Dynamical Systems: Fixed Points, Linearization, Invariant Manifolds, Bifurcations & Chaos

Solving PDEs with the Laplace Transform: The Wave Equation

Solving PDEs with the Laplace Transform: The Wave Equation

Solving PDEs with the Laplace Transform: The Heat Equation

Solving PDEs with the Laplace Transform: The Heat Equation

Moore's Law, exponential growth, and extrapolation!

Moore's Law, exponential growth, and extrapolation!

Chaotic Dynamical Systems

Chaotic Dynamical Systems

Numerical Integration of Chaotic Dynamics: Uncertainty Propagation & Vectorized Integration

Numerical Integration of Chaotic Dynamics: Uncertainty Propagation & Vectorized Integration

Coding a Fourth-Order Runge-Kutta Integrator in Python and Matlab

Coding a Fourth-Order Runge-Kutta Integrator in Python and Matlab

Runge-Kutta Integrator Overview: All Purpose Numerical Integration of Differential Equations

Runge-Kutta Integrator Overview: All Purpose Numerical Integration of Differential Equations

Numerical Integration of ODEs with Forward Euler and Backward Euler in Python and Matlab

Numerical Integration of ODEs with Forward Euler and Backward Euler in Python and Matlab

Stability of Forward Euler and Backward Euler Integration Schemes for Differential Equations

Stability of Forward Euler and Backward Euler Integration Schemes for Differential Equations

Error Analysis of Euler Integration Scheme for Differential Equations Using Taylor Series

Error Analysis of Euler Integration Scheme for Differential Equations Using Taylor Series

Deriving Forward Euler and Backward/Implicit Euler Integration Schemes for Differential Equations

Deriving Forward Euler and Backward/Implicit Euler Integration Schemes for Differential Equations

Numerical Simulation of Ordinary Differential Equations: Integrating ODEs

Numerical Simulation of Ordinary Differential Equations: Integrating ODEs

Why we can't take "dt" to 0 in a computer: Sources of error in numerical differentiation

Why we can't take "dt" to 0 in a computer: Sources of error in numerical differentiation

Numerical Integration: Discrete Riemann Integrals and Trapezoid Rule

Numerical Integration: Discrete Riemann Integrals and Trapezoid Rule

Numerical Differentiation: Second Derivatives and Differentiating Data

Numerical Differentiation: Second Derivatives and Differentiating Data

Numerical Differentiation with Finite Difference Derivatives

Numerical Differentiation with Finite Difference Derivatives

Forced Systems of Differential Equations in Matlab and Python

Forced Systems of Differential Equations in Matlab and Python

Linear Systems of Differential Equations with Forcing: Convolution and the Dirac Delta Function

Linear Systems of Differential Equations with Forcing: Convolution and the Dirac Delta Function

Systems of Differential Equations with Forcing: Example in Control Theory

Systems of Differential Equations with Forcing: Example in Control Theory

Differential Equations with Forcing: Method of Variation of Parameters

Differential Equations with Forcing: Method of Variation of Parameters

Differential Equations with Forcing: Method of Undetermined Coefficients

Differential Equations with Forcing: Method of Undetermined Coefficients

Systems of Differential Equations: Diagonalization and Jordan Canonical Form

Systems of Differential Equations: Diagonalization and Jordan Canonical Form

Repeated Eigenvalues and Secular Terms: Transient Growth in Non-Normal Systems

Repeated Eigenvalues and Secular Terms: Transient Growth in Non-Normal Systems

Non-Normal Linear Systems and Transient Energy Growth: Bypass Transition to Turbulence

Non-Normal Linear Systems and Transient Energy Growth: Bypass Transition to Turbulence

The Hartman-Grobman Theorem, Structural Stability of Linearization, and Stable/Unstable Manifolds

The Hartman-Grobman Theorem, Structural Stability of Linearization, and Stable/Unstable Manifolds

Measure-preserving EDMD: A 4-line structure-preserving & convergent DMD algorithm!

Measure-preserving EDMD: A 4-line structure-preserving & convergent DMD algorithm!

Residual Dynamic Mode Decomposition: A very easy way to get error bounds for your DMD computations

Residual Dynamic Mode Decomposition: A very easy way to get error bounds for your DMD computations

Phase Portrait for Double Well Potential

Phase Portrait for Double Well Potential

Drawing Phase Portraits for Nonlinear Systems

Drawing Phase Portraits for Nonlinear Systems

A Particle in a Potential Well: Nonlinear Dynamics

A Particle in a Potential Well: Nonlinear Dynamics

Linearizing Nonlinear Differential Equations Near a Fixed Point

Linearizing Nonlinear Differential Equations Near a Fixed Point

What is a "Linear" Differential Equation?

What is a "Linear" Differential Equation?

Stability and Eigenvalues: What does it mean to be a "stable" eigenvalue?

Stability and Eigenvalues: What does it mean to be a "stable" eigenvalue?

2x2 Systems of ODEs: Imaginary Eigenvalues and Center Fixed Points

2x2 Systems of ODEs: Imaginary Eigenvalues and Center Fixed Points

2x2 Systems of ODEs: Saddle Points and Instability

2x2 Systems of ODEs: Saddle Points and Instability

2x2 Systems of ODEs: Sources and Sinks

2x2 Systems of ODEs: Sources and Sinks

Solving Systems of Differential Equations with Eigenvalues and Eigenvectors

Solving Systems of Differential Equations with Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors

Motivating Eigenvalues and Eigenvectors with Differential Equations

Motivating Eigenvalues and Eigenvectors with Differential Equations

Matrix Systems of Differential Equations

Matrix Systems of Differential Equations

Solving General High-Order, Linear Ordinary Differential Equations (ODEs)

Solving General High-Order, Linear Ordinary Differential Equations (ODEs)

High-Order Ordinary Differential Equations with More Derivatives (from Physics)

High-Order Ordinary Differential Equations with More Derivatives (from Physics)

More Examples of Second Order Differential Equations

More Examples of Second Order Differential Equations

Engineering Math Pre-Req: Quick and Dirty Introduction to Python

Engineering Math Pre-Req: Quick and Dirty Introduction to Python

Engineering Math Pre-Req: Quick and Dirty Introduction to Matlab

Engineering Math Pre-Req: Quick and Dirty Introduction to Matlab

Example Second-Order ODE: Spring-Mass-Damper

Example Second-Order ODE: Spring-Mass-Damper

Second-Order Ordinary Differential Equations: Solving the Harmonic Oscillator Four Ways

Second-Order Ordinary Differential Equations: Solving the Harmonic Oscillator Four Ways

Taylor Series of the Exponential Function and Euler's Formula!

Taylor Series of the Exponential Function and Euler's Formula!

Taylor Series and Power Series Made Easy (with Pictures): Review of Calculus

Taylor Series and Power Series Made Easy (with Pictures): Review of Calculus

Solving Differential Equations with Power Series: A Simple Example

Solving Differential Equations with Power Series: A Simple Example

Interpretable Aeroelastic Models for Control at Insect Scale

Interpretable Aeroelastic Models for Control at Insect Scale

The Simplest Ordinary Differential Equation (ODE) and Its Exponential Solution

The Simplest Ordinary Differential Equation (ODE) and Its Exponential Solution

Gentle Introduction to Modeling with Matrices and Vectors: A Probabilistic Weather Model

Gentle Introduction to Modeling with Matrices and Vectors: A Probabilistic Weather Model

Calculus Review: The Derivative (and the Power Law and Chain Rule)

Calculus Review: The Derivative (and the Power Law and Chain Rule)

Differential Equations and Dynamical Systems: Overview

Differential Equations and Dynamical Systems: Overview

The Wave Equation and Slack Line Physics

The Wave Equation and Slack Line Physics

The Method of Characteristics and the Wave Equation

The Method of Characteristics and the Wave Equation

Solving the Wave Equation with Separation of Variables... and Guitar String Physics

Solving the Wave Equation with Separation of Variables... and Guitar String Physics

Deriving the Wave Equation

Deriving the Wave Equation

PDE 101: Separation of Variables! ...or how I learned to stop worrying and solve Laplace's equation

PDE 101: Separation of Variables! ...or how I learned to stop worrying and solve Laplace's equation

Physics-Informed Dynamic Mode Decomposition (PI-DMD)

Physics-Informed Dynamic Mode Decomposition (PI-DMD)

Enhancing Computational Fluid Dynamics with Machine Learning

Enhancing Computational Fluid Dynamics with Machine Learning

The Experimental Double Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control

The Experimental Double Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control

Discrepancy Modeling with Physics Informed Machine Learning

Discrepancy Modeling with Physics Informed Machine Learning

Deriving the Heat Equation in 2D & 3D (& in N Dimensions!) with Control Volumes and Vector Calculus

Deriving the Heat Equation in 2D & 3D (& in N Dimensions!) with Control Volumes and Vector Calculus

The Heat Equation and the Steady State Heat Distribution via Laplace's Equation

The Heat Equation and the Steady State Heat Distribution via Laplace's Equation

Deriving the Heat Equation: A Parabolic Partial Differential Equation for Heat Energy Conservation

Deriving the Heat Equation: A Parabolic Partial Differential Equation for Heat Energy Conservation

Laplace's Equation and Poisson's Equation

Laplace's Equation and Poisson's Equation

Partial Differential Equations Overview

Partial Differential Equations Overview

Potential Flow Part 2: Details and Examples

Potential Flow Part 2: Details and Examples

Laplace's Equation and Potential Flow

Laplace's Equation and Potential Flow

Are all vector fields the gradient of a potential? ... and the Helmholtz Decomposition

Are all vector fields the gradient of a potential? ... and the Helmholtz Decomposition

Stokes' Theorem and Green's Theorem

Stokes' Theorem and Green's Theorem

The Continuity Equation: A PDE for Mass Conservation, from Gauss's Divergence Theorem

The Continuity Equation: A PDE for Mass Conservation, from Gauss's Divergence Theorem

Gauss's Divergence Theorem

Gauss's Divergence Theorem

The Curl of a Vector Field: Measuring Rotation

The Curl of a Vector Field: Measuring Rotation

The Divergence of a Vector Field: Sources and Sinks

The Divergence of a Vector Field: Sources and Sinks

The Gradient Operator in Vector Calculus: Directions of Fastest Change & the Directional Derivative

The Gradient Operator in Vector Calculus: Directions of Fastest Change & the Directional Derivative

Div, Grad, and Curl: Vector Calculus Building Blocks for PDEs [Divergence, Gradient, and Curl]

Div, Grad, and Curl: Vector Calculus Building Blocks for PDEs [Divergence, Gradient, and Curl]

Vector Calculus and Partial Differential Equations: Big Picture Overview

Vector Calculus and Partial Differential Equations: Big Picture Overview

Deep Delay Autoencoders Discover Dynamical Systems w Latent Variables: Deep Learning meets Dynamics!

Deep Delay Autoencoders Discover Dynamical Systems w Latent Variables: Deep Learning meets Dynamics!

Sparse Nonlinear Models for Fluid Dynamics with Machine Learning and Optimization

Sparse Nonlinear Models for Fluid Dynamics with Machine Learning and Optimization

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

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

Overview of Deep Reinforcement Learning Methods

Overview of Deep Reinforcement Learning Methods

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

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

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

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

Reinforcement Learning Series: Overview of Methods

Reinforcement Learning Series: Overview of Methods

Robust Model Discovery with Ensemble Learning and SINDy (applications to active learning & control)

Robust Model Discovery with Ensemble Learning and SINDy (applications to active learning & control)

Machine Learning for Computational Fluid Dynamics

Machine Learning for Computational Fluid Dynamics

SINDy-PI: A robust algorithm for parallel implicit sparse identification of nonlinear dynamics

SINDy-PI: A robust algorithm for parallel implicit sparse identification of nonlinear dynamics

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

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

Sparse Nonlinear Dynamics Models with SINDy, Part 5: The Optimization Algorithms

Sparse Nonlinear Dynamics Models with SINDy, Part 5: The Optimization Algorithms

Sparse Nonlinear Dynamics Models with SINDy, Part 4: The Library of Candidate Nonlinearities

Sparse Nonlinear Dynamics Models with SINDy, Part 4: The Library of Candidate Nonlinearities

Sparse Nonlinear Dynamics Models with SINDy, Part 3: Effective Coordinates for Parsimonious Models

Sparse Nonlinear Dynamics Models with SINDy, Part 3: Effective Coordinates for Parsimonious Models

Sparse Nonlinear Dynamics Models with SINDy, Part 2: Training Data & Disambiguating Models

Sparse Nonlinear Dynamics Models with SINDy, Part 2: Training Data & Disambiguating Models

Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!

Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!

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

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

The Anatomy of a Dynamical System

The Anatomy of a Dynamical System

Sparse Sensor Placement Optimization for Classification

Sparse Sensor Placement Optimization for Classification

Interpretable Deep Learning for New Physics Discovery

Interpretable Deep Learning for New Physics Discovery

Sparse Sensor Placement Optimization for Reconstruction

Sparse Sensor Placement Optimization for Reconstruction

Sparse Representation (for classification) with examples!

Sparse Representation (for classification) with examples!

Deep Learning for Turbulence Closure Modeling

Deep Learning for Turbulence Closure Modeling

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

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

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

Turbulence: Reynolds Averaged Navier Stokes (RANS) Equations (Part 2, Momentum Equation)

Turbulence: Reynolds Averaged Navier Stokes (RANS) Equations (Part 2, Momentum Equation)

Turbulence: Reynolds Averaged Navier-Stokes (Part 1, Mass Continuity Equation)

Turbulence: Reynolds Averaged Navier-Stokes (Part 1, Mass Continuity Equation)

Turbulence is Everywhere! Examples of Turbulence and Canonical Flows

Turbulence is Everywhere! Examples of Turbulence and Canonical Flows

What Is Turbulence? Turbulent Fluid Dynamics are Everywhere

What Is Turbulence? Turbulent Fluid Dynamics are Everywhere

Deep Reinforcement Learning for Fluid Dynamics and Control

Deep Reinforcement Learning for Fluid Dynamics and Control

Deep Reinforcement Learning: Neural Networks for Learning Control Laws

Deep Reinforcement Learning: Neural Networks for Learning Control Laws

Reinforcement Learning: Machine Learning Meets Control Theory

Reinforcement Learning: Machine Learning Meets Control Theory

Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers

Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers

Robust Principal Component Analysis (RPCA)

Robust Principal Component Analysis (RPCA)

Robust Regression with the L1 Norm [Matlab]

Robust Regression with the L1 Norm [Matlab]

Robust Regression with the L1 Norm [Python]

Robust Regression with the L1 Norm [Python]

Robust Regression with the L1 Norm

Robust Regression with the L1 Norm

Robust, Interpretable Statistical Models: Sparse Regression with the LASSO

Robust, Interpretable Statistical Models: Sparse Regression with the LASSO

Beating Nyquist with Compressed Sensing, part 2

Beating Nyquist with Compressed Sensing, part 2

Beating Nyquist with Compressed Sensing

Beating Nyquist with Compressed Sensing

Beating Nyquist with Compressed Sensing, in Python

Beating Nyquist with Compressed Sensing, in Python

Shannon Nyquist Sampling Theorem

Shannon Nyquist Sampling Theorem

Machine Learning for Fluid Dynamics: Models and Control

Machine Learning for Fluid Dynamics: Models and Control

Machine Learning for Fluid Dynamics: Patterns

Machine Learning for Fluid Dynamics: Patterns

Data-driven nonlinear aeroelastic models of morphing wings for control

Data-driven nonlinear aeroelastic models of morphing wings for control

Data-Driven Resolvent Analysis

Data-Driven Resolvent Analysis

Machine Learning for Fluid Mechanics

Machine Learning for Fluid Mechanics

Data-driven Modeling of Traveling Waves

Data-driven Modeling of Traveling Waves

Underdetermined systems and compressed sensing [Python]

Underdetermined systems and compressed sensing [Python]

Underdetermined systems and compressed sensing [Matlab]

Underdetermined systems and compressed sensing [Matlab]

Sparsity and the L1 Norm

Sparsity and the L1 Norm

Compressed Sensing: When It Works

Compressed Sensing: When It Works

Compressed Sensing: Mathematical Formulation

Compressed Sensing: Mathematical Formulation

Compressed Sensing: Overview

Compressed Sensing: Overview

Sparsity and Parsimonious Models: Everything should be made as simple as possible, but no simpler

Sparsity and Parsimonious Models: Everything should be made as simple as possible, but no simpler

Modeling synchronization in turbulent flows

Modeling synchronization in turbulent flows