Applications of Monte Carlo Method in Science and Engineering

Aplicaciones del método de Monte Carlo en ciencia e ingeniería

En este libro se expone un amplio conjunto de aplicaciones de la simulación Monte Carlo en los campos de la física cuántica, la física estadística, la física médica, los materiales policristalinos, la química, la agricultura, el procesamiento de alimentos, la metalurgia, la electrodinámica, entre muchos otros tópicos. Está dividido en cuarenta capítulos distribuidos así:

• Monte Carlo Simulations in NDT
• Application of Monte Carlo Simulation in Optical Tweezers
• Enabling Grids for GATE Monte-Carlo Radiation Therapy Simulations with the GATE-Lab
• Monte Carlo Simulation for Ion Implantation Profiles, Amorphous Layer Thickness Formed by the Ion Implantation, and Database Based on Pearson Function
• Application of Monte Carlo Simulation in Industrial Microbiological Exposure Assessment
• Monte Carlo Simulation of Radiative Transfer in Atmospheric Environments for Problems Arising from Remote Sensing Measurements
• Monte Carlo Simulation of Pile-up Effect in Gamma Spectroscopy
• Monte Carlo Simulations of Microchannel Plate–Based, Time-Gated X-ray Imagers
• Many-particle Monte Carlo Approach to Electron Transport
• Monte-Carlo Simulation in Electron Microscopy and Spectroscopy
• Monte Carlo Simulation of SEM and SAM Images
• Monte Carlo Simulation of Insulating Gas Avalanche Development
• Monte Carlo Simulation of Electron Dynamics in Doped Semiconductors Driven by Electric Fields: Harmonic Generation, Hot-Carrier Noise and Spin Relaxation
• A Pearson Effective Potential for Monte-Carlo Simulation of Quantum Confinement Effects in nMOSFETs
• Monte Carlo Device Simulations
• Wang-Landau Algorithm and its Implementation for the Determination of Joint Density of States in Continuous Spin Models
• Characterizing Molecular Rotations using Monte Carlo Simulations
• Finite-time Scaling and its Applications to Continuous Phase Transitions
• Using Monte Carlo Method to Study Magnetic Properties of Frozen Ferrofluid
• Monte Carlo Studies of Magnetic Nanoparticles
• Monte Carlo Simulation for Magnetic Domain Structure and Hysteresis Properties
• Monte Carlo Simulations of Grain Growth in Polycrystalline Materials Using Potts Model
• Monte Carlo Simulations of Grain Growth in Metals
• Monte Carlo Simulations on Defects in Hard-Sphere Crystals Under Gravity
• Atomistic Monte Carlo Simulations in Steelmaking: High Temperature Carburization and Decarburization of Molten Steel
• GCMC Simulations of Gas Adsorption in Carbon Pore Structures
• Effect of the Repulsive Interactions on the Nucleation and Island Growth: Kinetic Monte Carlo Simulations
• Monte Carlo Methodology for Grand Canonical Simulations of Vacancies at Crystalline Defects
• Frequency-Dependent Monte Carlo Simulations of Phonon Transport in Nanostructures
• Performance Analysis of Adaptive GPS Signal Detection in Urban Interference Environment using the Monte Carlo Approach
• Practical Monte Carlo Based Reliability Analysis and Design Methods for Geotechnical Problems
• A Monte Carlo Framework to Simulate Multicomponent Droplet Growth by Stochastic Coalescence
• Monte Carlo Simulation of Room Temperature Ballistic Nanodevices
• Estimation of Optical Properties in Postharvest and Processing Technology
• MATLAB Programming of Polymerization Processes using Monte Carlo Techniques
• Monte Carlo Simulations in Solar Radio Astronomy
• Using Monte Carlo Simulation for Prediction of Tool Life
• Loss of Load Expectation Assessment in Electricity Markets using Monte Carlo Simulation and Neuro-Fuzzy Systems
• Automating First- and Second-order Monte Carlo Simulations for Markov Models in TreeAge Pro
• Monte Carlo Simulations of Adsorbed Molecules on Ionic Surfaces

Introduction to Monte Carlo simulation

Introducción a la simulación Monte Carlo

Este es un documento introductorio a la simulación Monte Carlo, aquella que se fundamenta en un muestreo aleatorio repetitivo y análisis estadístico para calcular resultados. Se describe de manera breve su naturaleza y relevancia, la forma de llevarla a cabo y analizar resultados, así como las técnicas matemáticas requeridas. También se presentan algunos ejemplos de distintas áreas donde se utiliza esta simulación.