{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129368"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129368","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient deep learning-based models for physics simulation and computational design","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Gladstone, Rini Jasmine"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Meidani, Hadi","Ravaioli, Umberto","Sychterz, Ann","Zhang, Shelly"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-20","date_published":"2025-02-20","updated_at":"2026-07-22T22:25:05Z","subjects":["Scientific computing","Deep learning","Machine learning","Graph neural networks, Physics informed neural networks","Topology optimization","Solid mechanics"],"languages":["en","eng"],"rights":["Copyright 2025 Rini Jasmine Gladstone"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129368","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meidani, Hadi","Ravaioli, Umberto","Sychterz, Ann","Zhang, Shelly"]},{"key":"dc:creator","label":"Author","values":["Gladstone, Rini Jasmine"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-20","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Scientific computing","Deep learning","Machine learning","Graph neural networks, Physics informed neural networks","Topology optimization","Solid mechanics"]}]},{"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 2025 Rini Jasmine Gladstone"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129368"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Rini Jasmine Gladstone, accepted the attached license on 2025-02-10 at 16:17.","The student, Rini Jasmine Gladstone, submitted this Dissertation for approval on 2025-02-10 at 16:30.","This Dissertation was approved for publication on 2025-02-20 at 14:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21640 on 2025-10-19 at 18:17:23","Scientific computing problems are widely used in an array of problems with applications ranging from micro- to macro-world in the field of physics, biology, material science, and civil and mechanical engineering. Many scientific computing problems such as design optimization, and design space exploration require resource-intensive repetitive simulation runs of a model with different input values. For systems characterized by numerous input parameters, the response calculation is particularly challenging as one also has to deal with the curse of dimensionality, which is the exponential increase in the volume of the input space, as the number of parameters increases linearly. Many real-world computational problems, require running a large number of high fidelity simulations of physical systems, governed by partial differential equations, using numerical methods such as finite element, which are often limited by the available computational resources. While data driven and physics-informed deep learning-based surrogate models have demonstrated success in tackling many of these problems, they still grapple with limitations in generalizability across problems, ability to handle complex domains and dependence on high data quality (for supervised models). The overarching objective of this dissertation is to take a step toward addressing these computational challenges and contribute to the promotion of efficient computational approaches based on deep learning for scientific computing problems such as physics simulations and computational design. In particular, and in moving toward this objective, I introduce various deep learning approaches, both data-driven and physics-informed, for fast and efficient modeling of physical systems. In the first part of the dissertation, I tackle supervised deep learning algorithms, primarily Variational Autoencoders (VAEs) and Graph Neural Networks (GNNs) and their applications for various array of problems such as Robust Topology Optimization (RTO), time-independent physics simulations and multi-fidelity methods. In particular, I introduce multi-fidelity architectures for VAE and GNN for improving the computational efficiency of the models. I also propose novel GNN architectures for accurate evaluation of time-independent physical systems. In the second part of the dissertation, I introduce two variants of physics-informed neural networks (PINNs), namely FO-PINN and PINN-FEM, to tackle some of the challenges of PINNs, including strong imposition of boundary conditions, solving higher-order PDEs and parameterized systems. A variety of engineering applications are considered to verify the accuracy and efficiency of the proposed methods."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Efficient deep learning-based models for physics simulation and computational design"]}]}],"canonical_facts":{"dc:contributor":["Meidani, Hadi","Ravaioli, Umberto","Sychterz, Ann","Zhang, Shelly"],"dc:creator":["Gladstone, Rini Jasmine"],"dc:date":["2025-02-20","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Rini Jasmine Gladstone, accepted the attached license on 2025-02-10 at 16:17.","The student, Rini Jasmine Gladstone, submitted this Dissertation for approval on 2025-02-10 at 16:30.","This Dissertation was approved for publication on 2025-02-20 at 14:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21640 on 2025-10-19 at 18:17:23","Scientific computing problems are widely used in an array of problems with applications ranging from micro- to macro-world in the field of physics, biology, material science, and civil and mechanical engineering. Many scientific computing problems such as design optimization, and design space exploration require resource-intensive repetitive simulation runs of a model with different input values. For systems characterized by numerous input parameters, the response calculation is particularly challenging as one also has to deal with the curse of dimensionality, which is the exponential increase in the volume of the input space, as the number of parameters increases linearly. Many real-world computational problems, require running a large number of high fidelity simulations of physical systems, governed by partial differential equations, using numerical methods such as finite element, which are often limited by the available computational resources. While data driven and physics-informed deep learning-based surrogate models have demonstrated success in tackling many of these problems, they still grapple with limitations in generalizability across problems, ability to handle complex domains and dependence on high data quality (for supervised models). The overarching objective of this dissertation is to take a step toward addressing these computational challenges and contribute to the promotion of efficient computational approaches based on deep learning for scientific computing problems such as physics simulations and computational design. In particular, and in moving toward this objective, I introduce various deep learning approaches, both data-driven and physics-informed, for fast and efficient modeling of physical systems. In the first part of the dissertation, I tackle supervised deep learning algorithms, primarily Variational Autoencoders (VAEs) and Graph Neural Networks (GNNs) and their applications for various array of problems such as Robust Topology Optimization (RTO), time-independent physics simulations and multi-fidelity methods. In particular, I introduce multi-fidelity architectures for VAE and GNN for improving the computational efficiency of the models. I also propose novel GNN architectures for accurate evaluation of time-independent physical systems. In the second part of the dissertation, I introduce two variants of physics-informed neural networks (PINNs), namely FO-PINN and PINN-FEM, to tackle some of the challenges of PINNs, including strong imposition of boundary conditions, solving higher-order PDEs and parameterized systems. A variety of engineering applications are considered to verify the accuracy and efficiency of the proposed methods."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129368"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Rini Jasmine Gladstone"],"dc:subject":["Scientific computing","Deep learning","Machine learning","Graph neural networks, Physics informed neural networks","Topology optimization","Solid mechanics"],"dc:title":["Efficient deep learning-based models for physics simulation and computational design"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}