Learning two-dimensional spatial dynamics from experimental data
Richards, Fred Christian
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https://hdl.handle.net/2142/23876
Description
Title
Learning two-dimensional spatial dynamics from experimental data
Author(s)
Richards, Fred Christian
Issue Date
1991
Doctoral Committee Chair(s)
Packard, Norman H.
Department of Study
Physics
Discipline
Physics
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
two-dimensional spatial dynamics
experimental physics
computer learning algorithm
dendritic solidification
Language
en
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.
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