{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117692"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117692","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning surrogate modeling methods in inverse high-speed link design","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Wang, Yuechen"],"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":["Chen, Xu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Signal Integrity","Inverse Problem","Machine Learning","Neural Network","High-speed Link Design"],"languages":["en","eng"],"rights":["Copyright 2022 Yuechen Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117692","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Xu"]},{"key":"dc:creator","label":"Author","values":["Wang, Yuechen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-09"]},{"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":["Signal Integrity","Inverse Problem","Machine Learning","Neural Network","High-speed Link Design"]}]},{"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 Yuechen Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117692"]}]},{"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 2024-12-01","The student, Yuechen Wang, accepted the attached license on 2022-12-09 at 13:40.","The student, Yuechen Wang, submitted this Thesis for approval on 2022-12-09 at 13:58.","This Thesis was approved for publication on 2022-12-09 at 14:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18794 on 2023-04-12 at 08:14:50","This thesis implements and compares the performance of several Machine Learning surrogate modeling methods for the inverse high-speed channel design problem. A Tandem Neural Network structure and a User-Choice Inverse Neural Network structure are purposed and thoroughly described for the inverse optimization of high-speed link problems. Comparisons are made between the newly purposed methods and the traditional Machine Learning methods. There are discussions on inverse optimization with mixed continuous-discrete variables. Three high-speed link designs are constructed as examples to evaluate the performance of the Machine Learning methods. Different types of error calculation are displayed to better compare the prediction results of different Machine Learning methods."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning surrogate modeling methods in inverse high-speed link design"]}]}],"canonical_facts":{"dc:contributor":["Chen, Xu"],"dc:creator":["Wang, Yuechen"],"dc:date":["2022-12","2022-12-09"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Yuechen Wang, accepted the attached license on 2022-12-09 at 13:40.","The student, Yuechen Wang, submitted this Thesis for approval on 2022-12-09 at 13:58.","This Thesis was approved for publication on 2022-12-09 at 14:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18794 on 2023-04-12 at 08:14:50","This thesis implements and compares the performance of several Machine Learning surrogate modeling methods for the inverse high-speed channel design problem. A Tandem Neural Network structure and a User-Choice Inverse Neural Network structure are purposed and thoroughly described for the inverse optimization of high-speed link problems. Comparisons are made between the newly purposed methods and the traditional Machine Learning methods. There are discussions on inverse optimization with mixed continuous-discrete variables. Three high-speed link designs are constructed as examples to evaluate the performance of the Machine Learning methods. Different types of error calculation are displayed to better compare the prediction results of different Machine Learning methods."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117692"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Yuechen Wang"],"dc:subject":["Signal Integrity","Inverse Problem","Machine Learning","Neural Network","High-speed Link Design"],"dc:title":["Machine learning surrogate modeling methods in inverse high-speed link design"],"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":"2026-07-22T22:24:56Z"}