2026-09-18T19:16:44Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117692 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Chen, Xu 2022-12 application/pdf en text 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 Machine learning surrogate modeling methods in inverse high-speed link design Wang, Yuechen 2022-12-09 Signal Integrity Inverse Problem Machine Learning Neural Network High-speed Link Design 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. Thesis eng https://hdl.handle.net/2142/117692 Copyright 2022 Yuechen Wang M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng