Stochastic simulation | Monte Carlo methods
The kinetic Monte Carlo (KMC) method is a Monte Carlo method computer simulation intended to simulate the time evolution of some processes occurring in nature. Typically these are processes that occur with known transition rates among states. It is important to understand that these rates are inputs to the KMC algorithm, the method itself cannot predict them. The KMC method is essentially the same as the dynamic Monte Carlo method and the Gillespie algorithm. (Wikipedia).
Monte Carlo Animation -- Day 3
This one looks good! -- Watch live at https://www.twitch.tv/simuleios
From playlist Determinant Visualization
What is the Monte Carlo method? | Monte Carlo Simulation in Finance | Pricing Options
In today's video we learn all about the Monte Carlo Method in Finance. These classes are all based on the book Trading and Pricing Financial Derivatives, available on Amazon at this link. https://amzn.to/2WIoAL0 Check out our website http://www.onfinance.org/ Follow Patrick on twitter h
From playlist Exotic Options & Structured Products
Monte Carlo Simulation For Any Model in Excel - A Step-by-Step Guide
Read more on Monte Carlo Simulations and download a sample model here: https://magnimetrics.com/monte-carlo-simulation-in-financial-modeling/ If you like this video, drop a comment, give it a thumbs up and consider subscribing here: https://www.youtube.com/channel/UCrdjXR70BwWIX--ZtQB42XQ
From playlist Excel Tutorials
Monte Carlo Animation -- Day 2
Still working on little things, animation now complete! -- Watch live at https://www.twitch.tv/simuleios
From playlist Determinant Visualization
Statistics: Ch 4 Probability and Statistics (66 of 74) What is a Monte Carlo Simulation?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn what is a Monte Carlo simulation. A simulation to model the probability of different outcomes when each outcome is diff
From playlist STATISTICS CH 4 STATISTICS IN PROBABILITY
Lecture 5: Introduction to Monte Carlo in Finance
Lecturer: Prof. Shimon Benninga Tel Aviv University 2.8.11
From playlist Financial Modeling (Simon Benniga)
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Manuel Athenes - Thermodynamic & transport properties of metal alloys: thermoelasticity & diffusion
Recorded 28 March 2023. Manuel Athenes of Commissariat à l'Énergie Atomique (CEA) - Centre d'Études Nucléaires de Saclay (CENS) presents "Conditioning schemes for accurately estimating thermodynamic and transport properties of metallic alloys: application to thermo-elasticity and elasto-di
From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing
David Ceperley - Introduction to Classical and Quantum Monte Carlo methods for Many-Body systems
Recorded 09 March 2022. David Ceperley of the University of Illinois at Urbana-Champaign presents "Introduction to Classical and Quantum Monte Carlo methods for Many-Body systems" at IPAM's Advancing Quantum Mechanics with Mathematics and Statistics Tutorials. Abstract: Metropolis (Markov
From playlist Tutorials: Advancing Quantum Mechanics with Mathematics and Statistics - March 8-11, 2022
35. Stochastic Chemical Kinetics 2
MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/10-34F15 Instructor: William Green This lecture finished up on the topic of stochastic chemical kinetics method. License: Creative Commons BY-NC-SA More information at http
From playlist MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015
Kinetic Sculpture Timelapse / Build UP LLC
A breath taking technology that combines the latest findings of engineering with the most innovative forms of arts. When colourful lights and ripples invade the body of hundreds of LED symmetric spheres, the kinetic sculpture starts floating in an overwhelming harmony, leaving the visitors
From playlist Kinetic Sculpture
Lec 23 | MIT 3.320 Atomistic Computer Modeling of Materials
Accelerated Molecular Dynamics, Kinetic Monte Carlo, and Inhomogeneous Spatial Coarse Graining View the complete course at: http://ocw.mit.edu/3-320S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 3.320 Atomistic Computer Modeling of Materials
AQC 2016 - Scaling Analysis & Instantons for Thermally-Assisted Tunneling and Quantum MC Simulations
A Google TechTalk, June 28, 2016, presented by Zhang Jiang (NASA) ABSTRACT: We develop an instantonic calculus to derive an analytical expression for the thermally-assisted tunneling decay rate of a metastable state in a fully connected quantum spin model. The tunneling decay problem can
From playlist Adiabatic Quantum Computing Conference 2016
Lecture Lorenzo Pareschi: Uncertainty quantification for kinetic equations II
The lecture was held within the of the Hausdorff Trimester Program: Kinetic Theory Abstract: In these lectures we overview some recent results in the field of uncertainty quantification for kinetic equations with random inputs. Uncertainties may be due to various reasons, like lack of kn
From playlist Summer School: Trails in kinetic theory: foundational aspects and numerical methods
Turbulence: Arrow of time & equilibrium-nonequilibrium behaviour by Mahendra Verma
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
Phase transitions in hard-core systems by Deepak Dhar ( Lecture - 1 )
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From playlist Bangalore School on Statistical Physics - X (2019)
Lecture Lorenzo Pareschi: Uncertainty quantification for kinetic equations III
The lecture was held within the of the Hausdorff Trimester Program: Kinetic Theory Abstract: In these lectures we overview some recent results in the field of uncertainty quantification for kinetic equations with random inputs. Uncertainties may be due to various reasons, like lack of kn
From playlist Summer School: Trails in kinetic theory: foundational aspects and numerical methods
Kinetic theory of stellar systems
https://www.sns.ias.edu/stellar-dynamics-workshop/schedule More videos on http://video.ias.edu
From playlist Natural Sciences