{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120193"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120193","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Subgrid modeling of general relativistic magnetohydrodynamic turbulence with physics informed deep learning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Rosofsky, Shawn Garrett"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Huerta, Eliu","Seidel, Edward","Allen, Gabrielle","Zhao, Zhizhen","Cooper, Lance"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Gravitational Waves","Numerical Relativity","Deep Learning","Neutron Stars","General Relativity","Magnetohydrodynamics"],"languages":["en","eng"],"rights":["Copyright 2022 Shawn Rosofsky"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120193","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huerta, Eliu","Seidel, Edward","Allen, Gabrielle","Zhao, Zhizhen","Cooper, Lance"]},{"key":"dc:creator","label":"Author","values":["Rosofsky, Shawn Garrett"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-01-04"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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."]},{"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":["Gravitational Waves","Numerical Relativity","Deep Learning","Neutron Stars","General Relativity","Magnetohydrodynamics"]}]},{"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 2022 Shawn Rosofsky"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120193"]}]},{"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 2023-09-01 without embargo terms","The student, Shawn Rosofsky, accepted the attached license on 2022-12-19 at 13:26.","The student, Shawn Rosofsky, submitted this Dissertation for approval on 2022-12-19 at 13:57.","This Dissertation was approved for publication on 2023-01-04 at 13:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18807 on 2023-09-01 at 17:06:50","The recent detections of gravitational waves (GWs) from mergers of binary black holes (BBH), binary neutron stars (BNS), and black hole neutron stars (BHNS) by LIGO have allowed us to view the universe like never before. BNS mergers in particular allow us to combine our GW observations with the electromagnetic (EM) counterparts to provide even more insight into these extreme events. To better understand the inner workings of neutron stars and BNS mergers, we would like to simulate BNS mergers from start to finish and obtain their GW signal along with their EM counterpart. We employ numerical relativity (NR) simulations to model the dynamic evolution of spacetime for this purpose. To model neutron stars, we add general relativistic magnetohydrodynamics (GRMHD) to these NR simulations to evolve the neutron stars and their magnetic fields in a curved spacetime. However, at the merger, the Kelvin-Helmholz instability (KHI) creates GRMHD turbulence that amplifies the magnetic field at a smaller scale than BNS simulations are capable of resolving. This prevents us from accurately modeling the EM counterparts of BNS mergers in NR simulation. This thesis seeks to address such GRMHD turbulence by using artificial neural networks (ANNs) as subgrid-scale (SGS) models for this turbulent amplification. We begin by examining these ANN based SGS models within the large eddy simulation (LES) formulation. We study how these ANN models perform on precomputed data and demonstrate that ANN models are superior to the state of the art traditional models of Newtonian magnetohydrodynamic (MHD) turbulence designed for this purpose. Then, we implemented these networks in actual simulations, illustrating their effect on the fluid evolution and their limitations. To overcome such limitations, we looked towards physics informed machine learning and physics informed neural operators (PINO) in particular. We tested PINOs on a wide variety of problems ranging from the wave equation to the nonlinear shallow water equations to assess their strengths and weaknesses. Encouraged by the flexibility and accuracy of PINOs, we applied PINOs to the Newtonian incompressible MHD equations and assessed their performance as we increased the kinetic and magnetic Reynolds numbers. The results of this thesis provides a stepping stone for future work in resolving GRMHD turbulence associated with the KHI. In addition, this thesis presents our direct contributions to the field of NR that are not directly related to the GRMHD turbulence problem. The first of these works on the necessary resolutions for NR neutron star simulations to compute the theoretically accurate frequencies and damping times of f-mode oscillations. We provide our contribution to two studies involving NR simulations of eccentric BBH mergers. One involves creating a large catalog of non-spinning eccentric BBH mergers. The other work focuses on running and closely examining a smaller set of spinning eccentric BBH simulations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Subgrid modeling of general relativistic magnetohydrodynamic turbulence with physics informed deep learning"]}]}],"canonical_facts":{"dc:contributor":["Huerta, Eliu","Seidel, Edward","Allen, Gabrielle","Zhao, Zhizhen","Cooper, Lance"],"dc:creator":["Rosofsky, Shawn Garrett"],"dc:date":["2023-05","2023-01-04"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Shawn Rosofsky, accepted the attached license on 2022-12-19 at 13:26.","The student, Shawn Rosofsky, submitted this Dissertation for approval on 2022-12-19 at 13:57.","This Dissertation was approved for publication on 2023-01-04 at 13:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18807 on 2023-09-01 at 17:06:50","The recent detections of gravitational waves (GWs) from mergers of binary black holes (BBH), binary neutron stars (BNS), and black hole neutron stars (BHNS) by LIGO have allowed us to view the universe like never before. BNS mergers in particular allow us to combine our GW observations with the electromagnetic (EM) counterparts to provide even more insight into these extreme events. To better understand the inner workings of neutron stars and BNS mergers, we would like to simulate BNS mergers from start to finish and obtain their GW signal along with their EM counterpart. We employ numerical relativity (NR) simulations to model the dynamic evolution of spacetime for this purpose. To model neutron stars, we add general relativistic magnetohydrodynamics (GRMHD) to these NR simulations to evolve the neutron stars and their magnetic fields in a curved spacetime. However, at the merger, the Kelvin-Helmholz instability (KHI) creates GRMHD turbulence that amplifies the magnetic field at a smaller scale than BNS simulations are capable of resolving. This prevents us from accurately modeling the EM counterparts of BNS mergers in NR simulation. This thesis seeks to address such GRMHD turbulence by using artificial neural networks (ANNs) as subgrid-scale (SGS) models for this turbulent amplification. We begin by examining these ANN based SGS models within the large eddy simulation (LES) formulation. We study how these ANN models perform on precomputed data and demonstrate that ANN models are superior to the state of the art traditional models of Newtonian magnetohydrodynamic (MHD) turbulence designed for this purpose. Then, we implemented these networks in actual simulations, illustrating their effect on the fluid evolution and their limitations. To overcome such limitations, we looked towards physics informed machine learning and physics informed neural operators (PINO) in particular. We tested PINOs on a wide variety of problems ranging from the wave equation to the nonlinear shallow water equations to assess their strengths and weaknesses. Encouraged by the flexibility and accuracy of PINOs, we applied PINOs to the Newtonian incompressible MHD equations and assessed their performance as we increased the kinetic and magnetic Reynolds numbers. The results of this thesis provides a stepping stone for future work in resolving GRMHD turbulence associated with the KHI. In addition, this thesis presents our direct contributions to the field of NR that are not directly related to the GRMHD turbulence problem. The first of these works on the necessary resolutions for NR neutron star simulations to compute the theoretically accurate frequencies and damping times of f-mode oscillations. We provide our contribution to two studies involving NR simulations of eccentric BBH mergers. One involves creating a large catalog of non-spinning eccentric BBH mergers. The other work focuses on running and closely examining a smaller set of spinning eccentric BBH simulations."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120193"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Shawn Rosofsky"],"dc:subject":["Gravitational Waves","Numerical Relativity","Deep Learning","Neutron Stars","General Relativity","Magnetohydrodynamics"],"dc:title":["Subgrid modeling of general relativistic magnetohydrodynamic turbulence with physics informed deep learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Physics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}