Equation-free modeling

B27 Introduction to linear models

Now that we finally now some techniques to solve simple differential equations, let's apply them to some real-world problems.

From playlist Differential Equations

2_2_8 Example problem with a linear equation modeling a real world situation

Solving an example problem for a linear equation. The equation models a real-world situation.

From playlist Differential Equations

Systems of linear equations

Systems of linear equations seek a common solution for the unknowns across more than one equation. It can be very simple to calculate a solution using simple algebra. Alternatively you can use elementary row operations or even lines and planes in two- and three-dimensional space. At th

From playlist Introducing linear algebra

Sebastian Reich (DDMCS@Turing): Learning models by making them interact

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

C53 Introduction to modelling

An introduction to modelling with higher order differential equations.

From playlist Differential Equations

B28 An example problem of a linear model

Here is our first real-world linear problem.

From playlist Differential Equations

Structural equation modeling in free software JASP

In this video, I will demonstrate how to do structural equation modeling in free software JASP. Useful links: JASP: https://jasp-stats.org/ Source 1: https://lavaan.ugent.be/tutorial/sem.html Source 2: https://www.routledge.com/Quantitative-Data-Analysis-for-Language-Assessment-Volume-II-

From playlist Structural Equation Modeling

Introduction to Population Models and Logistic Equation (Differential Equations 31)

https://www.patreon.com/ProfessorLeonard How differential equations can be applied to population models. We also explore the Logistic Equation, Population Explosion, and Population Extinction from a mathematical perspective involving limits.

From playlist Differential Equations

Differential Equations: Lecture 3.1 Linear Models

This is a real classroom lecture from the Differential Equations course I teach. I covered section 3.1 which is on linear models. Basically I introduced a specific linear model, then went on to focus on a specific one, Newton's Law of Cooling. This course uses Zill's book on DE's, this i

From playlist Differential Equations Full Lectures

Seminar In the Analysis and Methods of PDE (SIAM PDE): Benoit Perthame

The COVID-19 pandemic and consequent social distancing call for online venues of research dissemination. This webinar will serve as a way to recognize achievements in our area, and it will be expected to help promoting the standing of both SIMA and APDE. Further, these webinars should be b

From playlist Seminar In the Analysis and Methods of PDE (SIAM PDE)

Hao Shen (Wisconsin) -- Stochastic quantization, large N, and mean field limit

We study "large N problems” in quantum field theory using SPDE methods via stochastic quantization. In the SPDE setting this is formulated as mean field problems. We will consider the vector Phi^4 model (i.e. linear sigma model), whose stochastic quantization is a system of N coupled dynam

From playlist Columbia Probability Seminar

Dynamics and transport in integrable and nearly integrable models (Lecture 1) by Joel Moore

PROGRAM: INTEGRABLE SYSTEMS IN MATHEMATICS, CONDENSED MATTER AND STATISTICAL PHYSICS ORGANIZERS: Alexander Abanov, Rukmini Dey, Fabian Essler, Manas Kulkarni, Joel Moore, Vishal Vasan and Paul Wiegmann DATE : 16 July 2018 to 10 August 2018 VENUE: Ramanujan Lecture Hall, ICTS Bangalore

From playlist Integrable​ ​systems​ ​in​ ​Mathematics,​ ​Condensed​ ​Matter​ ​and​ ​Statistical​ ​Physics

Greg Pavliotis (DDMCS@Turing): Phase transitions for mean field limits of noisy interacting agents

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

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

Generalised hydrodynamics (Lecture 2) by Benjamin Doyon

PROGRAM THERMALIZATION, MANY BODY LOCALIZATION AND HYDRODYNAMICS ORGANIZERS: Dmitry Abanin, Abhishek Dhar, François Huveneers, Takahiro Sagawa, Keiji Saito, Herbert Spohn and Hal Tasaki DATE : 11 November 2019 to 29 November 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore How do is

From playlist Thermalization, Many Body Localization And Hydrodynamics 2019

Semiclassical Kinetic Theory of Integrable Systems by Vir Bulchandani

DISCUSSION MEETING : HYDRODYNAMICS AND FLUCTUATIONS - MICROSCOPIC APPROACHES IN CONDENSED MATTER SYSTEMS (ONLINE) ORGANIZERS: Abhishek Dhar (ICTS-TIFR, India), Keiji Saito (Keio University, Japan) and Tomohiro Sasamoto (Tokyo Institute of Technology, Japan) DATE: 06 September 2021 to 10

From playlist Hydrodynamics and fluctuations - microscopic approaches in condensed matter systems (ONLINE) 2021

Higher solutions of Hitchin’s selfduality equations and real sections by Sebastian Heller

DISCUSSION MEETING ANALYTIC AND ALGEBRAIC GEOMETRY DATE:19 March 2018 to 24 March 2018 VENUE:Madhava Lecture Hall, ICTS, Bangalore. Complex analytic geometry is a very broad area of mathematics straddling differential geometry, algebraic geometry and analysis. Much of the interactions be

From playlist Analytic and Algebraic Geometry-2018

Models of Fluid-Structure Interaction and Exact Controllability - M. Vanninathan

PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod

From playlist Data Assimilation Research Program

Roland Bauerschmidt - The Renormalisation Group - a Mathematical Perspective (1/4)

In physics, the renormalisation group provides a powerful point of view to understand random systems with strong correlations. Despite advances in a number of particular problems, in general its mathematical justification remains a holy grail. I will give an introduction to the main concep

From playlist Roland Bauerschmidt - The Renormalisation Group - a Mathematical Perspective

R - Structural Equation Model Basics Lecture 1

Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers the basic terminology for structural equation modeling including: identification, scaling, variable types, manifest/latent variables, path coefficient types, endogenous/exogenous variables, degrees o

From playlist Structural Equation Modeling