Nonlinear Dynamics: Estimating Embedding Parameters Part 1
These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos ...
Complexity Explorer
Nonlinear Dynamics: Delay Coordinate Embedding
These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos ...
Complexity Explorer
LabVIEW PID + Kalman Filter + MPC - Part4
https://halvorsen.blog/ https://halvorsen.blog/documents/automation/ https://halvorsen.blog/documents/automation/mpc/
Industrial IT and Automation
Nonlinear Dynamics: Caveats and Extensions
These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos ...
Complexity Explorer
Mod-08 Lec-32 Linear Stochastic Dynamics - Kalman Filter
Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit ...
nptelhrd
02417 Lecture 11 part B: Linear state space models
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ...
Lasse Engbo Christiansen
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 ...
Steve Brunton
02417 Lecture 11 part A: Introduction to state space models
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ...
Lasse Engbo Christiansen
State Space Models and Simulation in Python
Linear Time Invariant (LTI) state space models are a linear representation of a dynamic system in either discrete or continuous time. Putting a model into state ...
APMonitor.com
Time Series Modelling and State Space Models: Professor Chris Williams, University of Edinburgh
AR, MA and ARMA models - Parameter estimation for ARMA models - Hidden Markov Models (definitions, inference, learning) - Linear-Gaussian HMMs ...
The Alan Turing Institute
Steve Brunton: "Dynamical Systems (Part 1/2)"
Watch part 2/2 here: https://youtu.be/HgeC0-VIUtc Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Dynamical Systems (Part 1/2)" ...
Institute for Pure & Applied Mathematics (IPAM)
Feedback Control of Hybrid Dynamical Systems
Hybrid systems have become prevalent when describing complex systems that mix continuous and impulsive dynamics. Continuous dynamics usually govern ...
Society for Industrial and Applied Mathematics
Neural SDEs: Deep Generative Models in the Diffusion Limit - Maxim Raginsky
In deep generative models, the latent variable is generated by a time-inhomogeneous Markov chain, where at each time step we pass the current state through a ...
Institute for Advanced Study
Nonlinear Dynamics: Introduction to ODE Solvers
These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos ...
Complexity Explorer
Kalman Filter Demo and Theory. Pitch Angle Estimation on a myRio
Demonstration of a real-time Kalman filter using a myRio in real-time. The example considers the estimation of pitch angle fusing a Gyro and accelerometer ...
T.J Moir
From causal inference to autoencoders, memorization & gene regulation - Caroline Uhler, MIT
Recent progress in genomics makes it possible to perform perturbation experiments at a very large scale. This motivates the development of a causal inference ...
The Alan Turing Institute
Online Learning and Optimization from Continuous to Discrete Time
Many discrete algorithms for convex optimization and online learning can be interpreted as a discretization of a continuous-time process. Perhaps the simplest ...
Microsoft Research
Fast Nonlinear Estimation and Control
Fast estimation and control techniques are critical for real-time applications. Researchers at KU Leuven will share cutting-edge methods to apply Nonlinear ...
APMonitor.com
Modelling of Dynamical Systems - Control System Design 2/6
Mathematical modelling of a real-world, dynamical system (balanced aeropendulum) and actuators. From moment balances, to differential equations, to transfer ...
Phil’s Lab
Using curvature to understand the structure of dynamics
In this talk, we will discuss the application of curvature to understanding the shape of dynamics of an attractor -- specifically features like unstable hyperbolic ...
Santa Fe Institute
Jean-Jacques Slotine - Collective computation in nonlinear networks and the grammar of evolvability
Institut des Hautes Études Scientifiques (IHÉS)
System Identification: DMD Control Example
This lecture gives a Matlab example of dynamic mode decomposition with control (DMDc) for full-state system identification. Dynamic mode decomposition with ...
Steve Brunton
JuliaCon 2017 | Event-Based Simulation of Spiking Neural Networks | Rainer Engelken
Visit http://julialang.org/ to download Julia.
The Julia Programming Language
IACS Seminar: Bayesian Machine Learning Models for Understanding Microbiome Dynamics 9/20/19
Presented by Georg Gerber, Assistant Professor of Pathology at the Harvard Medical School and member of the Harvard-MIT Health Sciences and Technology ...
Harvard Institute for Applied Computational Science
Stochastic Optimal Control in Biology and Engineering
Control under uncertainty is a fundamental problem relevant to biology as well as engineering. Optimality models have explained numerous details of biological ...
UW Video
Lars Ruthotto: "Deep Neural Networks Motivated By Differential Equations (Part 1/2)"
Watch part 2/2 here: https://youtu.be/1mVycBKb1TE Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Deep Neural Networks Motivated ...
Institute for Pure & Applied Mathematics (IPAM)
ODE Parameter Estimation in Excel
Parameters (time constant and delay time) in a first order differential equation are fit to data in Excel. Excel solver is used to minimize a sum of squared errors ...
APMonitor.com
Stanford Seminar - Computational memory: A stepping-stone to non-von Neumann computing?
EE380: Computer Systems Colloquium Seminar Computational memory: A stepping-stone to non-von Neumann computing? Speaker: Abu Sebastian, IBM ...
stanfordonline
Estimating the Information Flow in Deep Neural Networks
Speaker: Ziv Goldfeld -- Postdoctoral Fellow, MIT Abstract: This talk will discuss the flow of information and the evolution of internal representations during deep ...
Stanford Research Talks
Ben Recht: "Trying to Make Sense of Control from Pixels"
Intersections between Control, Learning and Optimization 2020 "Trying to Make Sense of Control from Pixels" Ben Recht - University of California, Berkeley (UC ...
Institute for Pure & Applied Mathematics (IPAM)
Jeff Anderson | NCAR IMAGe | Building State-of-the-Art Forecast Systems with the Ensemble Kalman
The High Altitude Observatory (HAO) of the National Center for Atmospheric research (NCAR) is located in Boulder, Colorado, at the foot of the Rocky Mountains ...
High Altitude Observatory HAO | NCAR
Conférence : l'assimilation de données pour les géosciences
Conférence de Marc Bocquet, Professeur à à l'École des Ponts ParisTech, directeur adjoint du CEREA. L'assimilation de données est un ensemble de ...
ONERA
PID Control Tuning with Python GEKKO
PID Control is simulated and optimized with Python GEKKO. The three tuning constants: Kc, tauI, and tauD (or P=Kc, I=Kc/tauI, D=Kc*tauD) are adjusted either ...
APMonitor.com
Lightning Talks - Galen Cho, Christina Yu, Cyril Zhang, Laura Balzano, Max Simchovitz
Workshop on New Directions in Reinforcement Learning and Control Topic: Lightning Talks Speaker: Galen Cho, Christina Yu, Cyril Zhang, Laura Balzano, Max ...
Institute for Advanced Study
Nonlinear Estimation in MATLAB and Python
This tutorial is an example of estimating parameters of a highly nonlinear system from systems biology. It uses an initialization strategy to find a suitable ...
APMonitor.com
Control Fundamentals
Sean Meyn (University of Florida) https://simons.berkeley.edu/talks/tbd-185 Theory of Reinforcement Learning Boot Camp.
Simons Institute
Srinivasa Varadhan - The Abel Lecture - A Short History of Large Deviations
This lecture was held by Abel Laureate Srinivasa S.R. Varadhan at The University of Oslo, May 24, 2007 and was part of the Abel Prize Lectures in connection ...
The Abel Prize
Necmiye Ozay: "A fresh look at some classical system identification methods"
Intersections between Control, Learning and Optimization 2020 "A fresh look at some classical system identification methods" Necmiye Ozay - University of ...
Institute for Pure & Applied Mathematics (IPAM)
David Duvenaud (U of T) --Latent Stochastic Differential Equations
MIFODS - Machine Learning Seminar. Cambridge, US Oct 24, 2019.
MIFODS
Weinan E: "High Dimensional PDEs: Theory and Numerical Algorithms"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop I: High Dimensional Hamilton-Jacobi Methods in Control and Differential Games "High Dimensional ...
Institute for Pure & Applied Mathematics (IPAM)
Machine Learning Work Shop - Bayesian Nonparametrics for Complex Dynamical Phenomena
Machine Learning Work Shop-Session 3 - Emily Fox - 'Bayesian Nonparametrics for Complex Dynamical Phenomena' Markov switching processes, such as ...
Microsoft Research
Data Analyst Interview Questions And Answers | Data Analyst Interview Questions | Simplilearn
Data analyst is one of the trending jobs of the 21st century. This video covers all the important questions that would help you crack a data analyst interview.
Simplilearn
