{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T21:12:56Z","timestamp":1774473176960,"version":"3.50.1"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Advantest as part of the Graduate School \u201cIntelligent Methods for Test and Reliability\u201d (GS-IMTR) at the University of Stuttgart"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3675354","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T19:40:12Z","timestamp":1773862812000},"page":"43449-43462","source":"Crossref","is-referenced-by-count":0,"title":["Accelerating Transistor Simulations With Self-Supervised Graph Attention Networks"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4704-9274","authenticated-orcid":false,"given":"Tarek","family":"Mohamed","sequence":"first","affiliation":[{"name":"Semiconductor Test and Reliability (STAR), University of Stuttgart, Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5649-3102","authenticated-orcid":false,"given":"Hussam","family":"Amrouch","sequence":"additional","affiliation":[{"name":"Semiconductor Test and Reliability (STAR), University of Stuttgart, Stuttgart, Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2020.2987139"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-022-00881-0"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1143\/JJAP.43.3784"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SISPAD.2019.8870521"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.23919\/SISPAD49475.2020.9241661"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SISPAD54002.2021.9592540"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LED.2023.3290930"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202300435"},{"key":"ref9","article-title":"How to find your friendly neighborhood: Graph attention design with self-supervision","author":"Kim","year":"2022","journal-title":"arXiv:2204.04879"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/vlsit.2014.6894343"},{"key":"ref11","article-title":"Multi layer perceptron","author":"Riedmiller","year":"2019","journal-title":"arXiv:1908.01878"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/0470863803.ch1"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MLCAD58807.2023.10299886"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.23919\/SISPAD49475.2020.9241622"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1888"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/VLSITechnologyandCir46769.2022.9830392"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-27599-z"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2023.3316635"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref20","first-page":"7180","article-title":"Improving breadth-wise backpropagation in graph neural networks helps learning long-range dependencies","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lukovnikov"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1039"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM19574.2021.9720616"},{"key":"ref23","article-title":"Learning mesh-based simulation with graph networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Pfaff"},{"key":"ref24","article-title":"Learning physical simulation with message passing transformer","author":"Xu","year":"2024","journal-title":"arXiv:2406.06060"},{"key":"ref25","article-title":"Efficient learning of mesh-based physical simulation with BSMS-GNN","author":"Cao","year":"2023","journal-title":"arXiv:2210.02573"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.118794","article-title":"Human-computer interactive rehabilitation: A 3D graph deep learning method for non-contact gesture recognition in post-epidemic and aging societies","volume":"257","author":"Xing","year":"2026","journal-title":"Measurement"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3546874"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1002\/pamm.202200306"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2024.105607","article-title":"A two-stage CFD-GNN approach for efficient steady-state prediction of urban airflow and airborne contaminant dispersion","volume":"112","author":"Zhao","year":"2024","journal-title":"Sustain. Cities Soc."},{"key":"ref30","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017","journal-title":"arXiv:1609.02907"},{"key":"ref31","article-title":"Graph attention networks","author":"Velickovic","year":"2018","journal-title":"arXiv:1710.10903"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570455"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM.2013.6724592"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.7567\/JJAP.55.114201"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/mi11080780"},{"key":"ref36","article-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","volume":"14","author":"Kohavi","year":"2001"},{"key":"ref37","article-title":"Fast graph representation learning with PyTorch geometric","author":"Fey","year":"2019","journal-title":"arXiv:1903.02428"},{"issue":"1","key":"ref38","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1908.01878"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.114"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/HOTCHIPS.2008.7476520"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2018.2867721"},{"issue":"10","key":"ref43","doi-asserted-by":"crossref","first-page":"837","DOI":"10.3390\/nano14100837","article-title":"CMOS scaling for the 5 nm node and beyond: Device, process and technology","volume":"14","author":"Radamson","year":"2024","journal-title":"Nanomaterials"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11441355.pdf?arnumber=11441355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:57:42Z","timestamp":1774468662000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11441355\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3675354","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}