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
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
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
Bartosz Protas: "Max Amplification of Enstrophy in Navier-Stokes Flows & the Hydrodynamic Blow-U..."
Transport and Mixing in Complex and Turbulent Flows 2021 "Maximum Amplification of Enstrophy in Navier-Stokes Flows and the Hydrodynamic Blow-Up Problem" Bartosz Protas - McMaster University Abstract: This investigation concerns a systematic search for potentially singular behavior in 3D
From playlist Transport and Mixing in Complex and Turbulent Flows 2021
Leslie Smith: "Fast-Slow Coupling in Atmospheric Flows with Water"
Transport and Mixing in Complex and Turbulent Flows 2021 "Fast-Slow Coupling in Atmospheric Flows with Water" Leslie Smith - University of Wisconsin-Madison, Mathematics Abstract: Atmospheric variables (temperature, velocity, etc.) are often decomposed into balanced and unbalanced compon
From playlist Transport and Mixing in Complex and Turbulent Flows 2021
Randomness - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Step by step instructions showing how to run Ford-Fulkerson on a flow network. Sources: 1. http://www.win.tue.nl/~nikhil/courses/2WO08/07NetworkFlowI.pdf LinkedIn: https://www.linkedin.com/in/michael-sambol-076471ba
From playlist Maximum Flow Algos // Michael Sambol
Randomness Solution - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Networks: Part 7 - Oxford Mathematics 4th Year Student Lecture
Network Science provides generic tools to model and analyse systems in a broad range of disciplines, including biology, computer science and sociology. This course (we are showing the whole course over the next few weeks) aims at providing an introduction to this interdisciplinary field o
From playlist Oxford Mathematics Student Lectures - Networks
27c3: I Control Your Code (en)
Speaker: Mathias Payer Attack Vectors Through the Eyes of Software-based Fault Isolation Unsafe languages and an arms race for new bugs calls for an additional line of defense in software systems. User-space virtualization uses dynamic instrumentation to detect different attack vectors a
From playlist 27C3: We come in peace
The Generalized Ramanujan Conjectures and Applications (Lecture 3) by Peter Sarnak
Lecture 3: Mobius Randomness and Horocycle Dynamics Abstract : The Mobius function mu(n) is minus one to the number of distinct prime factors of n if n has no square factors and zero otherwise. Understanding the randomness (often referred to as the "Mobius randomness principle") in this f
From playlist Generalized Ramanujan Conjectures Applications by Peter Sarnak
Yang Liu: Fully Dynamic Electrical Flows: Sparse Maxflow Faster than Goldberg-Rao
We give an algorithm for computing exact maximum flows on graphs with m edges and integer capacities in the range [1,U] in ̃O(m^((3/2) −(1/328)) log U) time. For sparse graphs with polynomially bounded integer capacities, this is the first improvement over the ̃O(m^(1.5) log U) time bou
From playlist Workshop: Continuous approaches to discrete optimization
Sparse Nonlinear Models for Fluid Dynamics with Machine Learning and Optimization
Reduced-order models of fluid flows are essential for real-time control, prediction, and optimization of engineering systems that involve a working fluid. The sparse identification of nonlinear dynamics (SINDy) algorithm is being used to develop nonlinear models for complex fluid flows th
From playlist Data-Driven Dynamical Systems with Machine Learning
The appearance of noise like behaviour (...) systems - CEB T2 2017 - Liverani - 3/3
Carlangelo Liverani (Univ. Roma Tor Vergata) - 31/05/17 The appearance of noise like behaviour in deterministic dynamical systems I will discuss how noise can arise in deterministic systems with strong instability with respect to the initial conditions. Starting with a discussion of the C
From playlist 2017 - T2 - Stochastic Dynamics out of Equilibrium - CEB Trimester
PMSP - Pseudorandomness of the Mobius function - Peter Sarnak
Peter Sarnak Princeton University and Institute for Advanced Study June 18, 2010 For more videos, visit http://video.ias.edu
From playlist Mathematics
DDPS | Physics-Guided Deep Learning for Dynamics Forecasting
In this talk from July 9, 2021, University of California, San Diego Computer Science Ph.D. student Rui Wang discusses physics-based modeling with deep learning. Description: Modeling complex physical dynamics is a fundamental task in science and engineering. There is a growing need for in
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Péter Nándori: Mixing and the local central limit theorem for hyperbolic dynamical systems
Abstract: We present a convenient joint generalization of mixing and the local version of the central limit theorem (MLLT) for probability preserving dynamical systems. We verify that MLLT holds for several examples of hyperbolic systems by reviewing old results for maps and presenting new
From playlist Probability and Statistics
Exploring state-space topology in the geosciences - Sciamarella - Workshop 1 - CEB T3 2019
Sciamarella (CNRS) / 11.10.2019 Exploring state-space topology in the geosciences ************************************* Langue : Anglais ; Date : 11.10.2019; Conférencier : Sciamarella, Denisse; Évenement : Workshop 1 - CEB T3 2019; Lieu : IHP; Mots Clés :
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
On the representation of measures by Christian S. Rodrigues
PROGRAM SMOOTH AND HOMOGENEOUS DYNAMICS ORGANIZERS: Anish Ghosh, Stefano Luzzatto and Marcelo Viana DATE: 23 September 2019 to 04 October 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Ergodic theory has its origins in the the work of L. Boltzmann on the kinetic theory of gases.
From playlist Smooth And Homogeneous Dynamics
Bound-preserving numerical solutions of variable density two-phase flows
Date and Time: Thursday, November 11, 12:00pm Eastern time zone Speaker: Beatrice Riviere, Rice University Abstract: Modeling pore-scale flows modeling is important for many applications relevant to energy and environment. Phase-field models are popular models because they implicitly tra
From playlist SIAM Geosciences Webinar Series