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Title:Learning two-dimensional spatial dynamics from experimental data
Author(s):Richards, Fred Christian
Doctoral Committee Chair(s):Packard, Norman H.
Department / Program:Physics, General
Artificial Intelligence
Discipline:Physics, General
Artificial Intelligence
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Physics, General
Artificial Intelligence
Abstract:This thesis discusses the analysis of complex spatial dynamics using a computer learning algorithm. The goal is to model experimental data, the dendritic solidification of ammonium bromide crystals, using a learning algorithm to search through a space of possible models in order to find an optimal description of the data. The space of possible models is a class of probabilistic cellular automaton rules, a rule which is inherently local. The traditional definition of a cellular automaton has been enhanced here to include information which is non-local in both space and time thus allowing the models to reproduce a greater variety of complex spatial dynamics. The learning algorithm performing the stochastic search through the model space is a variation of the genetic algorithm. The technique is first applied to pattern data generated by deterministic models for the solidification process. Simple cellular automata and more complicated generalizations of cellular automata are used to generate test data for the learning algorithm. Video images of solidifying ammonium bromide dendrites are then modeled using the genetic algorithm, and the results are compared to the test cases.
Issue Date:1991
Rights Information:Copyright 1991 Richards, Fred Christian
Date Available in IDEALS:2011-05-07
Identifier in Online Catalog:AAI9124474
OCLC Identifier:(UMI)AAI9124474

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