Mathematical modeling

Deterministic simulation

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).

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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

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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

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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

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Monte Carlo Simulation and Python 12 - Checking Results

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From playlist Monte Carlo Simulation with Python

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Hamiltonian Simulation and Universal Quantum (...) - T. Cubitt - Main Conference - CEB T3 2017

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From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester

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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

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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

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Savitch's Theorem, Space Hierarchy

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From playlist [Shai Simonson]Theory of Computation

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Complexity Theory, Quantified Boolean Formula

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From playlist [Shai Simonson]Theory of Computation

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Monte Carlo Simulation and Python 2 - Dice Function

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From playlist Monte Carlo Simulation with Python

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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

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The Bullseye

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From playlist [Shai Simonson]Theory of Computation

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Stochastic and Deterministic Models for Tropical Convection - Boualem Khouider

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From playlist Mathematical Perspectives on Clouds, Climate, and Tropical Meteorology

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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

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Stochastic modelling of geophysical flows - Mémin - Workshop 2 - CEB T3 2019

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From playlist 2019 - T3 - The Mathematics of Climate and the Environment

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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

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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

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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

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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

Related pages

Systems theory | Random variable | Determinism | System dynamics | Dynamical system | Stochastic simulation | Randomness | Systems simulation | Statistical model | Dynamical systems theory | Variable (mathematics)