{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/23876"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/23876","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning two-dimensional spatial dynamics from experimental data","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Richards, Fred Christian"],"institution":null,"degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Packard, Norman H."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-12T15:49:33Z","date_published":"2011-05-12T15:49:33Z","updated_at":"2026-07-22T22:25:22Z","subjects":["two-dimensional spatial dynamics","experimental physics","computer learning algorithm","dendritic solidification"],"languages":["en"],"rights":["1991 Fred Christian Richards"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["3471835"],"render_values":[{"text":"3471835","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/23876","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Packard, Norman H."]},{"key":"dc:creator","label":"Author","values":["Richards, Fred Christian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-12T15:49:33Z","10000-01-01","1991"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation / Thesis","text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Physics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["two-dimensional spatial dynamics","experimental physics","computer learning algorithm","dendritic solidification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["1991 Fred Christian Richards"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["3471835","http://hdl.handle.net/2142/23876"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis discusses the analysis of complex spatial dynamics using a computer learning algorithm. 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