{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124564"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124564","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Drift Correcting Mulitphysics Informed Neural Network Coupled PDE Solver","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Wandke, Kevin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Z, Y","Raginsky, Maxim","Belabbas, Mohamed","Kim, Joohyung"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Physics Informed Neural Networks","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Kevin Wandke"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124564","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Z, Y","Raginsky, Maxim","Belabbas, Mohamed","Kim, Joohyung"]},{"key":"dc:creator","label":"Author","values":["Wandke, Kevin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-23"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Physics Informed Neural Networks","Machine Learning"]}]},{"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 2024 Kevin Wandke"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124564"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Kevin Wandke, accepted the attached license on 2024-04-23 at 12:47.","The student, Kevin Wandke, submitted this Dissertation for approval on 2024-04-23 at 13:45.","This Dissertation was approved for publication on 2024-04-23 at 15:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20578 on 2024-09-16 at 00:44:24","One of the core functions of computers has been their ability to perform modeling tasks that predict and explain the world. One of the most common approaches for doing this has been the finite element method. However, this approach is computationally costly and can be slow. In more recent years, parallel computing strategies have become a popular alternative. These approaches use neural networks or other machine learning methods to model and predict the behavior of systems. One of these techniques that is especially promising is the Physics-Informed Neural Network, or PINN. PINN uses a neural network to efficiently and accurately solve partial differential equations or PDEs. In this work, we introduce an improvement to the PINN method. This new approach is called the ``Drift Correcting Multiphysics Informed Neural Network”, or DCMPINN. DCMPINN has three novel enhancements that improve its ability to solve complex systems of PDEs. The first of these improvements is the ability to model nonlinear hyperelastic deformations. This functionality is critical for modeling the behavior of biological systems or soft robots. The second improvement is a novel system of solving PDEs over long timescales. First, the input domain is decomposed into several subdomains which allows for more efficient training on each subdomain. Then, an additional ``Drift Correcting\" network is trained to maintain continuity between different subdomains while ensuring that quantities of interest such as energy do not drift from their target values over the course of the simulation. The final improvement is the ability to solve coupled systems of PDEs. By assigning one or more terms of the neural network’s loss function to each type of physics that is to be modeled, coupled systems of PDEs can be efficiently solved. The improvements developed in DCMPINN represent a significant improvement over existing methods for solving coupled PDEs. This will allow computational resources to be more efficiently utilized in the creation of more complex and comprehensive models of real-world processes."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Drift Correcting Mulitphysics Informed Neural Network Coupled PDE Solver"]}]}],"canonical_facts":{"dc:contributor":["Z, Y","Raginsky, Maxim","Belabbas, Mohamed","Kim, Joohyung"],"dc:creator":["Wandke, Kevin"],"dc:date":["2024-05","2024-04-23"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Kevin Wandke, accepted the attached license on 2024-04-23 at 12:47.","The student, Kevin Wandke, submitted this Dissertation for approval on 2024-04-23 at 13:45.","This Dissertation was approved for publication on 2024-04-23 at 15:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20578 on 2024-09-16 at 00:44:24","One of the core functions of computers has been their ability to perform modeling tasks that predict and explain the world. One of the most common approaches for doing this has been the finite element method. However, this approach is computationally costly and can be slow. In more recent years, parallel computing strategies have become a popular alternative. These approaches use neural networks or other machine learning methods to model and predict the behavior of systems. One of these techniques that is especially promising is the Physics-Informed Neural Network, or PINN. PINN uses a neural network to efficiently and accurately solve partial differential equations or PDEs. In this work, we introduce an improvement to the PINN method. This new approach is called the ``Drift Correcting Multiphysics Informed Neural Network”, or DCMPINN. DCMPINN has three novel enhancements that improve its ability to solve complex systems of PDEs. The first of these improvements is the ability to model nonlinear hyperelastic deformations. This functionality is critical for modeling the behavior of biological systems or soft robots. The second improvement is a novel system of solving PDEs over long timescales. First, the input domain is decomposed into several subdomains which allows for more efficient training on each subdomain. Then, an additional ``Drift Correcting\" network is trained to maintain continuity between different subdomains while ensuring that quantities of interest such as energy do not drift from their target values over the course of the simulation. The final improvement is the ability to solve coupled systems of PDEs. By assigning one or more terms of the neural network’s loss function to each type of physics that is to be modeled, coupled systems of PDEs can be efficiently solved. The improvements developed in DCMPINN represent a significant improvement over existing methods for solving coupled PDEs. This will allow computational resources to be more efficiently utilized in the creation of more complex and comprehensive models of real-world processes."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124564"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Kevin Wandke"],"dc:subject":["Physics Informed Neural Networks","Machine Learning"],"dc:title":["Drift Correcting Mulitphysics Informed Neural Network Coupled PDE Solver"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}