In mathematical modeling, deterministic simulations contain no random variables and no degree of randomness, and consist mostly of equations, for example difference equations. These simulations have known inputs and they result in a unique set of outputs. Contrast stochastic (probability) simulation, which includes random variables. Deterministic simulation models are usually designed to capture some underlying mechanism or natural process. They are different from statistical models (for example linear regression) whose aim is to empirically estimate the relationships between variables. The deterministic model is viewed as a useful approximation of reality that is easier to build and interpret than a stochastic model. However, such models can be extremely complicated with large numbers of inputs and outputs, and therefore are often noninvertible; a fixed single set of outputs can be generated by multiple sets of inputs. Thus taking reliable account of parameter and model uncertainty is crucial, perhaps even more so than for standard statistical models, yet this is an area that has received little attention from statisticians. (Wikipedia).
A solar system, a simulation made with Excel
An Excel simulation of the solar system. You can see how things are recursively computed: the mutual gravity force from the locations, the accelerations, the velocities, and finally the updated locations. The solar eclipse is also shown. This is clip is intended to illustrate Chapter 24 Ap
From playlist Physics simulations
Simulation: The Challenge for Data Science
While machine learning has recently had dramatic successes, there is a large class of problems that it will never be able to address on its own. To test a policy proposal, for example, often requires understanding a counterfactual scenario that has never existed in the past, and that may
From playlist Turing Seminars
Hybrid Deterministic-Stochastic Modeling
Robert Nachbar explains how Mathematica and C were used to develop a hybrid deterministic-stochastic simulation engine based on differential equations and the chemical master equation. He highlights some interesting aspects of the implementation and demonstrates its use in this talk from t
From playlist Wolfram Technology Conference 2012
Monte Carlo Simulation and Python 12 - Checking Results
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 In the monte carlo simulation with Python series, we test various betting strategies. A simple 50/50 strategy, a martingale strategy, and the d'ale
From playlist Monte Carlo Simulation with Python
Hamiltonian Simulation and Universal Quantum (...) - T. Cubitt - Main Conference - CEB T3 2017
Toby Cubitt (UCL) / 14.12.2017 Title: Hamiltonian Simulation and Universal Quantum Hamiltonians Abstract: Physical (or "analogue") Hamiltonian simulation involves engineering a Hamiltonian of interest in the laboratory, and studying its properties experimentally (somewhat analogous to b
From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester
Monte Carlo Simulation and Python 10 -Analyzing some results
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 In the monte carlo simulation with Python series, we test various betting strategies. A simple 50/50 strategy, a martingale strategy, and the d'ale
From playlist Monte Carlo Simulation with Python
Quantum Simulation | You Can Program a Quantum Computer Too!
Quantum simulation is a really promising route to discover new technologies of the future by finding new materials with new physical properties. Check out the Qiskit YouTube channel here: https://www.youtube.com/qiskit and this is a good playlist to start with https://bit.ly/2KxqOIV I’ve
From playlist The Map of Quantum Physics Expanded
Savitch's Theorem, Space Hierarchy
Theory of Computation 16. Savitch's Theorem, Space Hierarchy ADUni
From playlist [Shai Simonson]Theory of Computation
Complexity Theory, Quantified Boolean Formula
Theory of Computation 15. Complexity Theory, Quantified Boolean Formula ADUni
From playlist [Shai Simonson]Theory of Computation
Monte Carlo Simulation and Python 2 - Dice Function
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 In this video, we begin creating our "roll dice" function. In the monte carlo simulation with Python series, we test various betting strategies. A
From playlist Monte Carlo Simulation with Python
6. TM Variants, Church-Turing Thesis
MIT 18.404J Theory of Computation, Fall 2020 Instructor: Michael Sipser View the complete course: https://ocw.mit.edu/18-404JF20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60_JNv2MmK3wkOt9syvfQWY Quickly reviewed last lecture. Showed that various TM variants are al
From playlist MIT 18.404J Theory of Computation, Fall 2020
Theory of Computation 11. The Bullseye ADUni
From playlist [Shai Simonson]Theory of Computation
Stochastic and Deterministic Models for Tropical Convection - Boualem Khouider
Stochastic and Deterministic Models for Tropical Convection Boualem Khouider, U.Victoria, Canada. DISCUSSION MEETING: MATHEMATICAL PERSPECTIVES ON CLOUDS, CLIMATE, AND TROPICAL METEOROLOGY MONDAY, 21 JANUARY, 2013 (PART 4)
From playlist Mathematical Perspectives on Clouds, Climate, and Tropical Meteorology
Christian P. Robert: The coordinate sampler: a non-reversible Gibbs-like MCMC sampler
VIRTUAL LECTURE Recording during the meeting "Quasi-Monte Carlo Methods and Applications " the November 05, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
Stochastic modelling of geophysical flows - Mémin - Workshop 2 - CEB T3 2019
Mémin (INRIA, FR) / 13.11.2019 Stochastic modelling of geophysical flows ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.com/InHe
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Monte Carlo Simulation and Python 7 - More comparison
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 In the monte carlo simulation with Python series, we test various betting strategies. A simple 50/50 strategy, a martingale strategy, and the d'ale
From playlist Monte Carlo Simulation with Python
Trend Modeling by Chiranjit Mukhopadhyay
Program Summer Research Program on Dynamics of Complex Systems ORGANIZERS: Amit Apte, Soumitro Banerjee, Pranay Goel, Partha Guha, Neelima Gupte, Govindan Rangarajan and Somdatta Sinha DATE : 15 May 2019 to 12 July 2019 VENUE : Madhava hall for Summer School & Ramanujan hall f
From playlist Summer Research Program On Dynamics Of Complex Systems 2019
Monte Carlo Simulation and Python 18 - 2D charting monte carlo variables
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 Here we use Matplotlib to chart a 2D representation of our variables and their relationship to profit. In the monte carlo simulation with Python
From playlist Monte Carlo Simulation with Python
Explanation of the butterfly effect and deterministic chaos using billiards
Created by George Datseris. In this relatively short education video I want to explain the butterfly effect and deterministic chaos at a fundamental level, using the simple and intuitive concept of billiards. Heavily inspired by 3Blue1Brown videos, and made as an entry for SoME1: https://
From playlist Summer of Math Exposition Youtube Videos