Data-Driven Control: Eigensystem Realization Algorithm
In this lecture, we introduce the eigensystem realization algorithm (ERA), which is a purely data-driven algorithm to obtain balanced input—output models from impulse response data. ERA was originally introduced to model aerospace structures, such as the Hubble Space Telescope and the Int
From playlist Data-Driven Control with Machine Learning
Data-Driven Control: Eigensystem Realization Algorithm Procedure
In this lecture, we describe the eigensystem realization algorithm (ERA) in detail, including step-by-step algorithmic instructions. https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
Discrete-Time Dynamical Systems
This video shows how discrete-time dynamical systems may be induced from continuous-time systems. https://www.eigensteve.com/
From playlist Data-Driven Dynamical Systems
Machine Learning Control: Genetic Algorithms
This lecture provides an overview of genetic algorithms, which can be used to tune the parameters of a control law. Machine Learning Control T. Duriez, S. L. Brunton, and B. R. Noack https://www.springer.com/us/book/9783319406237 Closed-Loop Turbulence Control: Progress and Challenges
From playlist Data-Driven Control with Machine Learning
Mind Reading with Intelligent & Adaptive UIs
What if you could predict user behavior with smart UIs? In this talk, we will explore how we can make adaptive and intelligent user interfaces that learn from how individual users use your apps, and personalize the interface and features just for them, in real-time. With probability-driven
From playlist Web Development
Data-Driven Resolvent Analysis
Benjamin Herrmann describes a data-driven algorithm to perform resolvent analysis from fluid mechanics to obtain the leading forcing and response modes, without recourse to the governing equations, but instead based on snapshots of the transient evolution of linearly stable flows. This ap
From playlist Data-Driven Science and Engineering
Simulating the Lorenz System in Matlab
This video shows how simple it is to simulate dynamical systems, such as the Lorenz system, in Matlab, using ode45. These lectures follow Chapter 7 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz Amazon: https://www.am
From playlist Data-Driven Dynamical Systems
Hankel Alternative View of Koopman (HAVOK) Analysis [FULL]
This video illustrates a new algorithm to decompose chaos into a linear system with intermittent forcing. This is based on the Hankel Alternative View of Koopman (HAVOK) analysis that builds linear regression models on eigen-time-delay coordinates. Chaos as an Intermittently Forced Line
From playlist Research Abstracts from Brunton Lab
Understanding Discrete Event Simulation, Part 4: Operations Research
Watch more MATLAB Tech Talks: https://goo.gl/ktpVB7 Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn the basics of using discrete-event simulation in operations research in this MATLAB® Tech Talk by Will Campbell. The
From playlist Understanding Discrete-Event Simulation - MATLAB Tech Talks
RubyConf 2010 - Consuming Gherkin: One Byte at a Time by: Greg Hnatiuk, Mike Sassak
The Ragel state machine compiler is a fantastic, Ruby-friendly tool for building compilers, parsers and the like, and is used by many popular programs including Mongrel, Cucumber, and Hpricot. But despite its pervasiveness, Ragel has a reputation for being arcane and difficult to understan
From playlist RubyConf 2010
Astrophysical fluid dynamics - James Stone
Members’ Seminar Topic: Astrophysical fluid dynamics Speaker: James Stone Affiliation: Professor, School of Natural Sciences Date: February 22, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
DDPS | 'No Equations, No Variables, No Parameters, No Space and No time' by Yannis Kevrekidis
Title: 'No Equations, No Variables, No Parameters, No Space and No time, Data and the Modeling of Complex Systems' Description: I will start by showing how several successful NN architectures (ResNets, recurrent nets, convolutional nets, autoencoders, neural ODEs, operator learning....) h
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Hankel Alternative View of Koopman (HAVOK) Analysis [SHORT]
This video illustrates a new algorithm to decompose chaos into a linear system with intermittent forcing. This is based on the Hankel Alternative View of Koopman (HAVOK) analysis that builds linear regression models on eigen-time-delay coordinates. Chaos as an Intermittently Forced Line
From playlist Research Abstracts from Brunton Lab
Overview lecture for series on data-driven control. In this lecture, we discuss how machine learning optimization can be used to discover models and effective controllers directly from data. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectur
From playlist Data-Driven Control with Machine Learning
Nature Reviews Physics: Machine learning in condensed matter and materials physics
Machine learning methods are now used in the simulation of the building blocks of matter: from the electronic- to the molecular-level structure. These tools have boosted well-known computational methods such as density functional theory or molecular dynamics simulation. These are expected
From playlist Nature Reviews Physics - AI for science and government (ASG) series
Coding Complex App Logic, Visually
What if you could code without coding? As the number of features and requirements increase in our apps, text-based logic becomes much more complex to understand, change, and test. Using visual event-driven state modeling with state machines and statecharts orchestrates this logic in a simp
From playlist Software Development
Data-Driven Dynamical Systems Overview
This video provides a high-level overview of this new series on data-driven dynamical systems. In particular, we explore the various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are 1)
From playlist Data-Driven Dynamical Systems with Machine Learning
Data-Driven Control: Balanced Models with ERA
In this lecture, we connect the eigensystem realization algorithm (ERA) to balanced proper orthogonal decomposition (BPOD). In particular, if enough data is collected, then ERA produces balanced models. https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
System Identification: Full-State Models with Control
This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and the sparse identification of nonlinear dynamics (SINDy). https://www.eigensteve.com/
From playlist Data-Driven Control with Machine Learning
