{"id":{"repo_id":"texas","oai_identifier":"oai:repositories.lib.utexas.edu:2152/132244"},"canonical_url":"https://search.dev.ndltd.org/etd/texas/oai:repositories.lib.utexas.edu:2152/132244","repository":{"repo_id":"texas","name":"University of Texas","base_url":"https://repositories.lib.utexas.edu/server/oai/request"},"display":{"title":"High-speed high-fidelity computational modeling approaches for cardiac applications","abstract":"This dissertation presents a high-speed, high-fidelity computational modeling approach for cardiac simulations, with a focus on replacement heart valves and left ventricular function. High-fidelity, structurally-based computational models were developed to simulate replacement heart valves. Through a structural constitutive model for electrospun biomaterials, the study discovered the presence of novel fiber-fiber interactions and the absence of fiber-gel interactions in electrospun biomaterials. Moreover, the model parameters were related to parameters controllable in the manufacturing process, presenting a way to narrow down optimal biomaterials in-silico. A comprehensive simulation pipeline was also developed to simulate BHV response under cycling loading upto 50 million cycles. The model predicted lasting changes to BHV leaflet shape and underlying structure within the first 20 million cycles, and found that these changes stabilize thereafter, a crucial insight that must be incorporated during BHV design. While these models are capable of providing excellent insights, their practical clinical applications using traditional simulation methods are impeded due to their prohibitively slow speed. To overcome these computational challenges, a novel neural network finite element approach was developed for high-speed high-fidelity cardiac simulations. This approach learns a family of solutions to a parametric PDE describing cardiac mechanics. The novelty of our model lies in 1) learning the underlying physics directly from the weak form of the PDE, either through the potential energy or the virtual work formulation, 2) no reliance on any additional experimental or simulation generated data for accurate predictions, 3) simultaneous training over the complete physiological loading range, 4) prediction accuracy of 0.1\\% relative to the conventional finite element method along with a prediction time of a few seconds and 5) NURBS mapping integrated within the method to accurately capture complex geometry. Previous research has repeatedly demonstrated the importance of a full heart model for accurate heart valve simulations, hence this study also includes the development of a NNFE model for a heart valve leaflet and a left ventricle, serving as intermediate steps towards more sophisticated full cardiac models. Finally, an NNFE model was developed to simulate effects of myocardial infarction in the left ventricle. While this work is a proof-of-concept on idealized geometries, there is a clear potential to extend it to realistic shapes and simulate additional variables including variable geometry and material properties. This approach overcomes the computational challenges in practical applications of high-fidelity cardiac computational models and opens pathways for patient-specific clinical diagnosis and treatment planning.","abstract_html":"This dissertation presents a high-speed, high-fidelity computational modeling approach for cardiac simulations, with a focus on replacement heart valves and left ventricular function. High-fidelity, structurally-based computational models were developed to simulate replacement heart valves. Through a structural constitutive model for electrospun biomaterials, the study discovered the presence of novel fiber-fiber interactions and the absence of fiber-gel interactions in electrospun biomaterials. Moreover, the model parameters were related to parameters controllable in the manufacturing process, presenting a way to narrow down optimal biomaterials in-silico. A comprehensive simulation pipeline was also developed to simulate BHV response under cycling loading upto 50 million cycles. The model predicted lasting changes to BHV leaflet shape and underlying structure within the first 20 million cycles, and found that these changes stabilize thereafter, a crucial insight that must be incorporated during BHV design. While these models are capable of providing excellent insights, their practical clinical applications using traditional simulation methods are impeded due to their prohibitively slow speed. To overcome these computational challenges, a novel neural network finite element approach was developed for high-speed high-fidelity cardiac simulations. This approach learns a family of solutions to a parametric PDE describing cardiac mechanics. The novelty of our model lies in 1) learning the underlying physics directly from the weak form of the PDE, either through the potential energy or the virtual work formulation, 2) no reliance on any additional experimental or simulation generated data for accurate predictions, 3) simultaneous training over the complete physiological loading range, 4) prediction accuracy of 0.1\\% relative to the conventional finite element method along with a prediction time of a few seconds and 5) NURBS mapping integrated within the method to accurately capture complex geometry. Previous research has repeatedly demonstrated the importance of a full heart model for accurate heart valve simulations, hence this study also includes the development of a NNFE model for a heart valve leaflet and a left ventricle, serving as intermediate steps towards more sophisticated full cardiac models. Finally, an NNFE model was developed to simulate effects of myocardial infarction in the left ventricle. While this work is a proof-of-concept on idealized geometries, there is a clear potential to extend it to realistic shapes and simulate additional variables including variable geometry and material properties. This approach overcomes the computational challenges in practical applications of high-fidelity cardiac computational models and opens pathways for patient-specific clinical diagnosis and treatment planning.","abstract_has_math":false,"creators":["Motiwale, Shruti"],"institution":"The University of Texas at Austin","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Sacks, Michael S."],"committee_chairs":[],"committee_members":["Kenneth Diller","Nicholas P Fey","Yen-Hsi Richard Tsai"],"year":2024,"date_issued":"2024-12","date_published":"2024-12","updated_at":"2026-07-24T05:01:06Z","subjects":["Replacement heart valves","Neural networks","Left ventricle","Scientific machine learning","Myocardial infarction"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.26153/tsw/59588"],"render_values":[{"text":"https://doi.org/10.26153/tsw/59588","href":"https://doi.org/10.26153/tsw/59588","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/2152/132244","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sacks, Michael S."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kenneth Diller","Nicholas P Fey","Yen-Hsi Richard Tsai"]},{"key":"dc:creator","label":"Author","values":["Motiwale, Shruti"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-04T14:56:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-04T14:56:19Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Texas at Austin"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Replacement heart valves","Neural networks","Left ventricle","Scientific machine learning","Myocardial infarction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2152/132244","https://doi.org/10.26153/tsw/59588"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation presents a high-speed, high-fidelity computational modeling approach for cardiac simulations, with a focus on replacement heart valves and left ventricular function. High-fidelity, structurally-based computational models were developed to simulate replacement heart valves. Through a structural constitutive model for electrospun biomaterials, the study discovered the presence of novel fiber-fiber interactions and the absence of fiber-gel interactions in electrospun biomaterials. Moreover, the model parameters were related to parameters controllable in the manufacturing process, presenting a way to narrow down optimal biomaterials in-silico. A comprehensive simulation pipeline was also developed to simulate BHV response under cycling loading upto 50 million cycles. The model predicted lasting changes to BHV leaflet shape and underlying structure within the first 20 million cycles, and found that these changes stabilize thereafter, a crucial insight that must be incorporated during BHV design. While these models are capable of providing excellent insights, their practical clinical applications using traditional simulation methods are impeded due to their prohibitively slow speed. To overcome these computational challenges, a novel neural network finite element approach was developed for high-speed high-fidelity cardiac simulations. This approach learns a family of solutions to a parametric PDE describing cardiac mechanics. The novelty of our model lies in 1) learning the underlying physics directly from the weak form of the PDE, either through the potential energy or the virtual work formulation, 2) no reliance on any additional experimental or simulation generated data for accurate predictions, 3) simultaneous training over the complete physiological loading range, 4) prediction accuracy of 0.1\\% relative to the conventional finite element method along with a prediction time of a few seconds and 5) NURBS mapping integrated within the method to accurately capture complex geometry. Previous research has repeatedly demonstrated the importance of a full heart model for accurate heart valve simulations, hence this study also includes the development of a NNFE model for a heart valve leaflet and a left ventricle, serving as intermediate steps towards more sophisticated full cardiac models. Finally, an NNFE model was developed to simulate effects of myocardial infarction in the left ventricle. While this work is a proof-of-concept on idealized geometries, there is a clear potential to extend it to realistic shapes and simulate additional variables including variable geometry and material properties. This approach overcomes the computational challenges in practical applications of high-fidelity cardiac computational models and opens pathways for patient-specific clinical diagnosis and treatment planning."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["High-speed high-fidelity computational modeling approaches for cardiac applications"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sacks, Michael S."],"dc:contributor.committeemember":["Kenneth Diller","Nicholas P Fey","Yen-Hsi Richard Tsai"],"dc:creator":["Motiwale, Shruti"],"dc:date.accessioned":["2025-04-04T14:56:19Z"],"dc:date.available":["2025-04-04T14:56:19Z"],"dc:date.issued":["2024-12"],"dc:description.abstract":["This dissertation presents a high-speed, high-fidelity computational modeling approach for cardiac simulations, with a focus on replacement heart valves and left ventricular function. High-fidelity, structurally-based computational models were developed to simulate replacement heart valves. Through a structural constitutive model for electrospun biomaterials, the study discovered the presence of novel fiber-fiber interactions and the absence of fiber-gel interactions in electrospun biomaterials. Moreover, the model parameters were related to parameters controllable in the manufacturing process, presenting a way to narrow down optimal biomaterials in-silico. A comprehensive simulation pipeline was also developed to simulate BHV response under cycling loading upto 50 million cycles. The model predicted lasting changes to BHV leaflet shape and underlying structure within the first 20 million cycles, and found that these changes stabilize thereafter, a crucial insight that must be incorporated during BHV design. While these models are capable of providing excellent insights, their practical clinical applications using traditional simulation methods are impeded due to their prohibitively slow speed. To overcome these computational challenges, a novel neural network finite element approach was developed for high-speed high-fidelity cardiac simulations. This approach learns a family of solutions to a parametric PDE describing cardiac mechanics. The novelty of our model lies in 1) learning the underlying physics directly from the weak form of the PDE, either through the potential energy or the virtual work formulation, 2) no reliance on any additional experimental or simulation generated data for accurate predictions, 3) simultaneous training over the complete physiological loading range, 4) prediction accuracy of 0.1\\% relative to the conventional finite element method along with a prediction time of a few seconds and 5) NURBS mapping integrated within the method to accurately capture complex geometry. Previous research has repeatedly demonstrated the importance of a full heart model for accurate heart valve simulations, hence this study also includes the development of a NNFE model for a heart valve leaflet and a left ventricle, serving as intermediate steps towards more sophisticated full cardiac models. Finally, an NNFE model was developed to simulate effects of myocardial infarction in the left ventricle. While this work is a proof-of-concept on idealized geometries, there is a clear potential to extend it to realistic shapes and simulate additional variables including variable geometry and material properties. This approach overcomes the computational challenges in practical applications of high-fidelity cardiac computational models and opens pathways for patient-specific clinical diagnosis and treatment planning."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2152/132244","https://doi.org/10.26153/tsw/59588"],"dc:language.iso":["English"],"dc:subject":["Replacement heart valves","Neural networks","Left ventricle","Scientific machine learning","Myocardial infarction"],"dc:title":["High-speed high-fidelity computational modeling approaches for cardiac applications"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["The University of Texas at Austin"]},"updated_at":"2026-07-24T05:01:06Z"}