{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125760"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125760","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"DAVMAS-GP: Domain aware variance minimizing Gaussian process regression for complex monostatic RCS prediction","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","abstract_html":"Submission published under a 24 month embargo labeled &#x27;Closed Access&#x27;, the embargo will last until 2026-08-01","abstract_has_math":false,"creators":["Jao, Kenneth"],"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":["Peng, Zhen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-19","date_published":"2024-07-19","updated_at":"2026-07-22T22:25:02Z","subjects":["Computational Electromagnetics","Covariance Function","Gaussian Process Regression (gpr)","Surrogate Model","Nonstationary Kernel","Variance Minimization","Adaptive Sampler","Radar Cross Section (rcs)"],"languages":["en","eng"],"rights":["Copyright 2024 Kenneth Jao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125760","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Zhen"]},{"key":"dc:creator","label":"Author","values":["Jao, Kenneth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-19","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Computational Electromagnetics","Covariance Function","Gaussian Process Regression (gpr)","Surrogate Model","Nonstationary Kernel","Variance Minimization","Adaptive Sampler","Radar Cross Section (rcs)"]}]},{"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 Kenneth Jao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125760"]}]},{"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 2026-08-01","The student, Kenneth Jao, accepted the attached license on 2024-06-24 at 14:49.","The student, Kenneth Jao, submitted this Thesis for approval on 2024-07-05 at 19:36.","This Thesis was approved for publication on 2024-07-19 at 10:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20865 on 2025-02-04 at 21:25:04","We present our method DAVMAS-GP, a Domain Aware Variance Minimizing Adaptive Sampling Gaussian Process for the prediction of RCS characteristics, including the complex vertically (VV) and horizontally (HH) polarized scattered far-field in both the angular and frequency domains. The method uses Gaussian process regression at its core, but employs the usage of nonstationary kernels, and our adaptive sampler VMAS to attain high accuracy predictions under extremely sparse sampling conditions. We validate our method with an aircraft model which exhibits complex scattering phenomena. Numerical results show that DAVMAS-GP is able to reduce the predictive root-mean-square-error (RMSE) by at least 98.5% compared to traditional methods of combining a Matérn kernel with non-informed Latin Hypercube sampling (LHS). With VMAS, < 1% RMSE is achieved using only 2% of samples. Allowing up to 4% of samples enables < 0.05% RMSE, across all vertical and horizontal complex components."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["DAVMAS-GP: Domain aware variance minimizing Gaussian process regression for complex monostatic RCS prediction"]}]}],"canonical_facts":{"dc:contributor":["Peng, Zhen"],"dc:creator":["Jao, Kenneth"],"dc:date":["2024-07-19","2024-08"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","The student, Kenneth Jao, accepted the attached license on 2024-06-24 at 14:49.","The student, Kenneth Jao, submitted this Thesis for approval on 2024-07-05 at 19:36.","This Thesis was approved for publication on 2024-07-19 at 10:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20865 on 2025-02-04 at 21:25:04","We present our method DAVMAS-GP, a Domain Aware Variance Minimizing Adaptive Sampling Gaussian Process for the prediction of RCS characteristics, including the complex vertically (VV) and horizontally (HH) polarized scattered far-field in both the angular and frequency domains. The method uses Gaussian process regression at its core, but employs the usage of nonstationary kernels, and our adaptive sampler VMAS to attain high accuracy predictions under extremely sparse sampling conditions. We validate our method with an aircraft model which exhibits complex scattering phenomena. Numerical results show that DAVMAS-GP is able to reduce the predictive root-mean-square-error (RMSE) by at least 98.5% compared to traditional methods of combining a Matérn kernel with non-informed Latin Hypercube sampling (LHS). With VMAS, < 1% RMSE is achieved using only 2% of samples. Allowing up to 4% of samples enables < 0.05% RMSE, across all vertical and horizontal complex components."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125760"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Kenneth Jao"],"dc:subject":["Computational Electromagnetics","Covariance Function","Gaussian Process Regression (gpr)","Surrogate Model","Nonstationary Kernel","Variance Minimization","Adaptive Sampler","Radar Cross Section (rcs)"],"dc:title":["DAVMAS-GP: Domain aware variance minimizing Gaussian process regression for complex monostatic RCS prediction"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:25:02Z"}