University of Illinois Urbana-Champaign

Learning two-dimensional spatial dynamics from experimental data

Richards, Fred Christian

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/23576

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, General
  • Artificial Intelligence
Discipline
  • Physics, General
  • Artificial Intelligence
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Physics, General
  • Artificial Intelligence
Language
eng
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.
Type of Resource
text
Permalink
http://hdl.handle.net/2142/23576
Copyright and License Information
Copyright 1991 Richards, Fred Christian

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Physics
Dissertations in Physics