Métodos de Monte Carlo modernos para sistemas de espines desordenados
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2024
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Abstract
En el presente trabajo consideraremos el problema de encontrar el estado de mínima energía para vidrios de espín mediante un método de Monte Carlo de cadena de Markov, llamado Parallel Tempering. El objetivo es, en primer lugar, construir un programa en C capaz de alcanzar el equilibrio térmico a temperaturas suficientemente bajas para alcanzar el estado fundamental de varias muestras de vidrios de espín, y, en segundo lugar, estudiar una propiedad termodinámica llamada “caos en temperatura” (que presenta características completamente diferentes en cada muestra), correlacionándola con el coste computacional de alcanzar el equilibrio térmico. Se obtendrán unos resultados con los que comparar con la literatura y con los que caracterizar mejor el caos de una muestra a partir de su curva del parámetro del caos.
In the present work we will consider the problem of finding the minimum energy state for spin glasses by means of a Markov chain Monte Carlo method, called Parallel Tempering. The objective is, first, to construct a C program capable of reaching thermal equilibrium at temperatures low enough to reach the fundamental state of several samples of spin glasses, and, second, to study a thermodynamic property called “temperature chaos” (which presents completely different characteristics in each sample), correlating it with the computational cost of reaching thermal equilibrium. Results will be obtained with which to compare with the literature and better characterize the chaos of a sample based on its chaos parameter curve.
In the present work we will consider the problem of finding the minimum energy state for spin glasses by means of a Markov chain Monte Carlo method, called Parallel Tempering. The objective is, first, to construct a C program capable of reaching thermal equilibrium at temperatures low enough to reach the fundamental state of several samples of spin glasses, and, second, to study a thermodynamic property called “temperature chaos” (which presents completely different characteristics in each sample), correlating it with the computational cost of reaching thermal equilibrium. Results will be obtained with which to compare with the literature and better characterize the chaos of a sample based on its chaos parameter curve.