Analytic and Monte Carlo random walk assessments of neutron fission chains and the probability of extinction

2021 ◽  
Vol 142 ◽  
pp. 104008
Author(s):  
Joe W. Durkee
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Neutron Fission ◽  
Probability Of Extinction ◽  
Monte Carlo Random Walk

Adsorption of non-ionic surfactants on organoclays in drilling fluid investigated by molecular descriptors and Monte Carlo random walk simulations

2021 ◽  
Vol 538 ◽  
pp. 148154
Author(s):  
Dina Kania ◽  
Robiah Yunus ◽  
Rozita Omar ◽  
Suraya Abdul Rashid ◽  
Badrul Mohamed Jan ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Molecular Descriptors ◽  
Drilling Fluid ◽  
Ionic Surfactants ◽  
Monte Carlo Random Walk

Monte Carlo random walk study of recombination and desorption of hydrogen on Si(111)

1985 ◽  
Vol 83 (3) ◽  
pp. 1382-1391 ◽  
Author(s):  
I. NoorBatcha ◽  
Lionel M. Raff ◽  
Donald L. Thompson
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Monte Carlo Random Walk

scholarly journals ¿Determinísticamente probable o probabilísticamente determinista?

UVserva ◽  
2018 ◽  
Author(s):  
Gerardo Mario Ortigoza Capetillo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Walk ◽  
Harmonic Function ◽  
Heat Equation ◽  
Stochastic Models ◽  
Generating Functions ◽  
Boundary Values ◽  
Partial Differential ◽  
Monte Carlo Random Walk

Este trabajo presenta la revisión de algunos modelos que conocemos como determinísticos o como estocásticos, así como algunas relaciones entre ellos, las cuales resultan interesantes. Vemos cómo las caminatas aleatorias generan algunas ecuaciones diferenciales parciales tales como la ecuación de calor; se presenta la ecuación de Laplace resuelta usando el juego Tour du wino; es decir, simulación Montecarlo para obtener los valores de una función armónica, como los promedios de su valores en la frontera obtenidos por diferentes trayectorias. Se revisan los modelos de Black and Scholes, así como el método de funciones generadoras de probabilidad para mostrar como determinados problemas probabilísticos pueden resolverse usando métodos determinísticos basados en ecuaciones diferenciales ordinarias y parciales.Palabras clave: modelos determinísticos, modelos probabilísticos, Montecarlo, caminatas aleatorias, Black and ScholesAbstract This work presents a review of some deterministic and stochastic models, interesting rela­tionships between them, are also discussed. Random walks give rise to partial differential mo­dels such as the heat equation. A Tour du wino game is introduced to approximate a solution to Laplace equation, here a Monte Carlo simulation is used to obtain the values of an harmonic function as the average of its boundary values using random trajectories. We review the Black & Scholes and the probability generating functions models to show how some probabilistic pro­blems can be solved using deterministics methods (based on ordinary and partial differential equations).Keywords: BDeterministic models; Stochastic models; Monte Carlo; random walk; Black and Scholes

DBpedia-Based Entity Linking via Greedy Search and Adjusted Monte Carlo Random Walk

2017 ◽  
Vol 36 (2) ◽  
pp. 1-34 ◽  
Author(s):  
Ming Liu ◽  
Lei Chen ◽  
Bingquan Liu ◽  
Guidong Zheng ◽  
Xiaoming Zhang
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Entity Linking ◽  
Greedy Search ◽  
Monte Carlo Random Walk

scholarly journals 4. The Evolution of Comet Orbits as Perturbed by Uranus and Neptune

1977 ◽  
Vol 39 ◽  
pp. 99-104 ◽  
Author(s):  
E. Everhart
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Computation Time ◽  
Orbital Evolution ◽  
Simulation Approach ◽  
Numerical Integrations ◽  
Distribution Shape ◽  
Short Period ◽  
Monte Carlo Random Walk ◽  
Comet Orbit

When the perturbing planets are Uranus and Neptune, the perturbations on comets are so much weaker than with Jupiter and Saturn that a study of the comets’ orbital evolution, using exact numerical integration, would require 200 times more revolutions. This is hardly practical with present computers. Here we describe results with a simulation approach, the “Monte Carlo (random walk) method.” The proper distribution shape for the perturbations in energy are found from a few thousand numerical integrations, then this distribution of perturbations is applied to millions of simulated orbit-revolutions. This method reproduces earlier Jupiter results in 1/500 the former computation time. We find that Neptune can capture near-parabolic comets with perihelia in the range of 30 to 34 AU, increasing their 1/a-values and decreasing their perihelia until they reach a region where Uranus can interact. Uranus in turn passes some of these on to Saturn, who passes some to Jupiter. Ultimately a few reach the orbits of the visible short-period comets. The process requires about 200,000 comet orbit-revolutions, 4 × 108 years, and the efficiency is one in 6000. The rest of the comets are ejected on hyperbolic orbits.

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