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Learning two-dimensional spatial dyn...

Richards, Fred Christian.

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Learning two-dimensional spatial dynamics from experimental data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning two-dimensional spatial dynamics from experimental data./
Author:	Richards, Fred Christian.
Description:	122 p.
Notes:	Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1489.
Contained By:	Dissertation Abstracts International52-03B.
Subject:	Physics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=9124474

Learning two-dimensional spatial dynamics from experimental data.
Richards, Fred Christian.

Learning two-dimensional spatial dynamics from experimental data. - 122 p.

Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1489.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1991.

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.Subjects--Topical Terms:

1018488
Physics, General.

Learning two-dimensional spatial dynamics from experimental data.
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008 130614s1991 eng d
035 $a (UnM)AAI9124474
035 $a AAI9124474
040 $a UnM $c UnM
100 1 $a Richards, Fred Christian. $3 1944246
245 1 0 $a Learning two-dimensional spatial dynamics from experimental data.
300 $a 122 p.
500 $a Source: Dissertation Abstracts International, Volume: 52-03, Section: B, page: 1489.
500 $a Adviser: N. Packard.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1991.
520 $a 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.
590 $a School code: 0090.
650 4 $a Physics, General. $3 1018488
650 4 $a Artificial Intelligence. $3 769149
690 $a 0605
690 $a 0800
710 2 0 $a University of Illinois at Urbana-Champaign. $3 626646
773 0 $t Dissertation Abstracts International $g 52-03B.
790 1 0 $a Packard, N., $e advisor
790 $a 0090
791 $a Ph.D.
792 $a 1991
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=9124474