Sequential dynamical system

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

The Anatomy of a Dynamical System

Dynamical systems are how we model the changing world around us. This video explores the components that make up a dynamical system. Follow updates on Twitter @eigensteve website: eigensteve.com

From playlist Research Abstracts from Brunton Lab

Linear systems: 2 equations, 2 unknowns

Basic introduction on how to solve linear systems of equations. Several examples are discussed and geometrically depicted through Geogebra.

From playlist Intro to Linear Systems of Simultaneous Equations

Queue Data Structure – Algorithms

This is an explanation of the dynamic data structure known as a queue. It compares a linear queue implemented by means of a dynamic array with a linear queue implemented with a static array. It also includes an explanation of how a circular queue works, along with pseudocode for the enqu

From playlist Data Structures

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

This video provides a high-level overview of dynamical systems, which describe the changing world around us. Topics include nonlinear dynamics, linearization at fixed points, eigenvalues and eigenvectors, bifurcations, invariant manifolds, and chaos!! @eigensteve on Twitter eigensteve.co

From playlist Dynamical Systems (with Machine Learning)

Carlangelo Liverani: Fast-Slow partially hyperbolic systems: an example

Abstract: I will discuss the simplest possible (non trivial) example of a fast-slow partially hyperbolic system with particular emphasis on the problem of establishing its statistical properties. Recording during the meeting : "Non Uniformly Hyperbolic Dynamical Systems. Coupling and Rene

From playlist Dynamical Systems and Ordinary Differential Equations

Chaotic Dynamical Systems

This video introduces chaotic dynamical systems, which exhibit sensitive dependence on initial conditions. These systems are ubiquitous in natural and engineering systems, from turbulent fluids to the motion of objects in the solar system. Here, we discuss how to recognize chaos and how

From playlist Engineering Math: Differential Equations and Dynamical Systems

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

Intro to Linear Systems: 2 Equations, 2 Unknowns - Dr Chris Tisdell Live Stream

Free ebook http://tinyurl.com/EngMathYT Basic introduction to linear systems. We discuss the case with 2 equations and 2 unknowns. A linear system is a mathematical model of a system based on the use of a linear operator. Linear systems typically exhibit features and properties that ar

From playlist Intro to Linear Systems

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

This video discusses the various machine learning optimization schemes that may be used for the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm. We discuss the LASSO sparse regression, sequential thresholded least squares (STLS), and the sparse relaxed regularized regression

From playlist Data-Driven Dynamical Systems with Machine Learning

Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 6 - Reinforcement Learning Primer

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ 0:00 Introduction 0:46 Logistics 2:31 Why Reinforcement Learning? 3:37 The Pla

From playlist Stanford CS330: Deep Multi-Task and Meta Learning

Weinan E: "Machine learning based multi-scale modeling"

Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Machine learning based multi-scale modeling" Weinan E - Princeton University, Mathematics Abstract: We will discuss a general methodology for developing reliable and in

From playlist Machine Learning for Physics and the Physics of Learning 2019

Deep Learning of Dynamics and Coordinates with SINDy Autoencoders

This video by Kathleen Champion describes a new approach for simultaneously discovering models and an effective coordinate system using a custom SINDy autoencoder. Paper at PNAS: https://www.pnas.org/content/116/45/22445.abstract Kathleen Champion, Bethany Lusch, J. Nathan Kutz, Steven L

From playlist Research Abstracts from Brunton Lab

Sequential Stopping for Parallel Monte Carlo by Peter W Glynn

PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear

From playlist Advances in Applied Probability 2019

"Diffusion Approximation and Sequential Experimentation" by Victor Araman

We consider a Bayesian sequential experimentation problem. We identify environments in which the average number of experiments that is conducted per unit of time is large and the informativeness of each individual experiment is low. Under such regimes, we derive a diffusion approximation f

From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management​

DDPS | Machine Learning and Multi-scale Modeling

Description: Multi-scale modeling is an ambitious program that aims at unifying the different physical models at different scales for the practical purpose of developing accurate models and simulation protocols for properties of interest. Although the concept of multi-scale modeling is ver

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

CDIS 4017 - Models of Speech Production Part 2 (DONE)

Chaya Guntupalli (Nanjundeswaran) Ph.D. CDIS 4017 - Speech and Hearing Science I ETSU Online Programs - http://www.etsu.edu/online

From playlist ETSU: CDIS 4017 - Speech and Hearing Science I | CosmoLearning Audiology

Sandro Vaienti - Thermodynamic formalism for open random dynamical systems - IPAM at UCLA

Recorded 01 September 2022. Sandro Vaienti of the Université de Toulon et du Var presents "Thermodynamic formalism for open random dynamical systems" at IPAM's Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond. Abstract: We consider random open dynamical sy

From playlist 2022 Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond

GTAC 2015: Multithreaded Test Synthesis

http://g.co/gtac Slides: https://drive.google.com/file/d/0ByHjpj7XroZ5dGJKUHp3NHM0Ylk/view Murali Krishna Ramanathan (Indian Institute of Science, Bangalore) Subtle concurrency errors in multithreaded libraries that arise because of incorrect or inadequate synchronization are often diffi

From playlist GTAC 2015

Review of Linear Time Invariant Systems

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Review: systems, linear systems, time invariant systems, impulse response and convolution, linear constant-coefficient difference equations

From playlist Introduction and Background