{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122269"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122269","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"TranDynaMo: A comparative study of transformer and GRU performance in modeling dynamical systems","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Pandey, Amogh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Mehr, Negar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Transformers, Large Language Models","Chatgpt","Gpt","Llm","Dynamics Learning","Dynamics Modeling","Rnn","Gru","Control System Dynamics","Dynamical Models"],"languages":["en","eng"],"rights":["Copyright 2023 Amogh Pandey"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122269","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mehr, Negar"]},{"key":"dc:creator","label":"Author","values":["Pandey, Amogh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-06"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Transformers, Large Language Models","Chatgpt","Gpt","Llm","Dynamics Learning","Dynamics Modeling","Rnn","Gru","Control System Dynamics","Dynamical Models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Amogh Pandey"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122269"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","The student, Amogh Pandey, accepted the attached license on 2023-12-05 at 15:44.","The student, Amogh Pandey, submitted this Thesis for approval on 2023-12-05 at 15:56.","This Thesis was approved for publication on 2023-12-06 at 11:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20148 on 2024-03-01 at 13:57:23","Dynamics modeling is critical to many robotics and control tasks and applications, such as motion planning and trajectory optimization. However, obtaining a model through current methods is difficult and expensive, so instead, there have been many efforts to come up with data-driven methods to learn dynamics models. With the popularity and success of Large Language Models (LLMs) such as ChatGPT, we were inspired to find a way to leverage transformers, the building blocks of ChatGPT, to solve the dynamics learning problem. In this thesis, we study the viability of utilizing transformers for dynamics modeling. We do this by first introducing transformers and posing dynamics learning as a sequence-to-sequence modeling task. We then create a GPT2-based dynamics learning framework called TranDynaMo and test its dynamics modeling performance for various dynamical systems, such as chaotic systems and systems with limit cycles, by giving it an initial condition and seeing how well it can predict the trajectory. We then compare TranDynaMo against a GRU-based framework. We find that when the dynamics learning framework is required to predict parts of a trajectory significantly beyond the horizon of the training trajectories it was given, TranDynaMo beats the GRU-based framework; however, if the training and testing trajectories are of almost the same horizon, both perform satisfactorily, but the GRU-based framework outperforms TranDynaMo, thus indicating that the generalization of transformers is better."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["TranDynaMo: A comparative study of transformer and GRU performance in modeling dynamical systems"]}]}],"canonical_facts":{"dc:contributor":["Mehr, Negar"],"dc:creator":["Pandey, Amogh"],"dc:date":["2023-12","2023-12-06"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","The student, Amogh Pandey, accepted the attached license on 2023-12-05 at 15:44.","The student, Amogh Pandey, submitted this Thesis for approval on 2023-12-05 at 15:56.","This Thesis was approved for publication on 2023-12-06 at 11:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20148 on 2024-03-01 at 13:57:23","Dynamics modeling is critical to many robotics and control tasks and applications, such as motion planning and trajectory optimization. However, obtaining a model through current methods is difficult and expensive, so instead, there have been many efforts to come up with data-driven methods to learn dynamics models. With the popularity and success of Large Language Models (LLMs) such as ChatGPT, we were inspired to find a way to leverage transformers, the building blocks of ChatGPT, to solve the dynamics learning problem. In this thesis, we study the viability of utilizing transformers for dynamics modeling. We do this by first introducing transformers and posing dynamics learning as a sequence-to-sequence modeling task. We then create a GPT2-based dynamics learning framework called TranDynaMo and test its dynamics modeling performance for various dynamical systems, such as chaotic systems and systems with limit cycles, by giving it an initial condition and seeing how well it can predict the trajectory. We then compare TranDynaMo against a GRU-based framework. We find that when the dynamics learning framework is required to predict parts of a trajectory significantly beyond the horizon of the training trajectories it was given, TranDynaMo beats the GRU-based framework; however, if the training and testing trajectories are of almost the same horizon, both perform satisfactorily, but the GRU-based framework outperforms TranDynaMo, thus indicating that the generalization of transformers is better."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122269"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Amogh Pandey"],"dc:subject":["Transformers, Large Language Models","Chatgpt","Gpt","Llm","Dynamics Learning","Dynamics Modeling","Rnn","Gru","Control System Dynamics","Dynamical Models"],"dc:title":["TranDynaMo: A comparative study of transformer and GRU performance in modeling dynamical systems"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}