{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:38:39Z","timestamp":1784133519659,"version":"3.55.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>High-quality passive devices are becoming increasingly important for the development of mobile devices and telecommunications, but obtaining such devices through simulation and analysis of electromagnetic (EM) behavior is time-consuming. To address this challenge, artificial neural network (ANN) models have emerged as an effective tool for modeling EM behavior, with NeuroTF being a representative example. However, these models are limited by the specific form of the transfer function, leading to discontinuity issues and high sensitivities. Moreover, previous methods have overlooked the physical relationship between distributed parameters, resulting in unacceptable numeric errors in the conversion results. To overcome these limitations, we propose two different neural network architectures: DeepOTF and ComplexTF. DeepOTF is a data-driven deep operator network for automatically learning feasible transfer functions for different geometric parameters. ComplexTF utilizes complex-valued neural networks to fit feasible transfer functions for different geometric parameters in the complex domain while maintaining causality and passivity. Our approach also employs an Equations-constraint Learning scheme to ensure the strict consistency of predictions and a dynamic weighting strategy to balance optimization objectives. The experimental results demonstrate that our framework shows superior performance than baseline methods, achieving up to 1,700\u00d7 higher accuracy.<\/jats:p>","DOI":"10.1145\/3663476","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T11:16:41Z","timestamp":1714562201000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["DeepOTF: Learning Equations-constrained Prediction for Electromagnetic Behavior"],"prefix":"10.1145","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5756-3494","authenticated-orcid":false,"given":"Peng","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6239-6774","authenticated-orcid":false,"given":"Siyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Huawei Technologies Noah's Ark Lab Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9195-6619","authenticated-orcid":false,"given":"Tinghuan","family":"Chen","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong - Shenzhen, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9457-9583","authenticated-orcid":false,"given":"Guojin","family":"Chen","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7348-5625","authenticated-orcid":false,"given":"Tsungyi","family":"Ho","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-4810","authenticated-orcid":false,"given":"Bei","family":"Yu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"237","volume-title":"Solid State Physics","author":"Hong Yang-Ki","year":"2013","unstructured":"Yang-Ki Hong and Jaejin Lee. 2013. Ferrites for RF passive devices. In Solid State Physics. Vol. 64. Elsevier, 237\u2013329."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICECS.2016.7841198"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMAG.2010.2044376"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMAG.1975.1058898"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAPT.2007.898747"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/20.334261"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1223824"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44709-1_21"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2012.2219031"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2007.909141"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2013.2253793"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2003.820898"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/22.989983"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2009.2032476"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2015.2504099"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2990157"},{"key":"e_1_3_1_18_2","first-page":"1","volume-title":"IEEE International Microwave Symposium (IMS\u201912)","author":"Guo Zhiyu","year":"2012","unstructured":"Zhiyu Guo, Jianjun Gao, Yazi Cao, and Qi-jun Zhang. 2012. Passivity enforcement for passive component modeling subject to variations of geometrical parameters using neural networks. In IEEE International Microwave Symposium (IMS\u201912). 1\u20133."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00302-5"},{"key":"e_1_3_1_20_2","volume-title":"International Conference on Learning Representations (ICLR\u201921)","author":"Li Zongyi","year":"2021","unstructured":"Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, and Anima Anandkumar. 2021. Fourier neural operator for parametric partial differential equations. In International Conference on Learning Representations (ICLR\u201921)."},{"key":"e_1_3_1_21_2","volume-title":"International Conference on Learning Representations (ICLR\u201920)","author":"Anandkumar Anima","year":"2020","unstructured":"Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li, Burigede Liu, and Andrew Stuart. 2020. Neural operator: Graph kernel network for partial differential equations. In International Conference on Learning Representations (ICLR\u201920)."},{"key":"e_1_3_1_22_2","volume-title":"Annual Conference on Neural Information Processing Systems (NIPS\u201920)","author":"Li Zongyi","year":"2020","unstructured":"Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, and Anima Anandkumar. 2020. Multipole graph neural operator for parametric partial differential equations. In Annual Conference on Neural Information Processing Systems (NIPS\u201920)."},{"key":"e_1_3_1_23_2","article-title":"A causality-DeepONet for causal responses of linear dynamical systems","author":"Liu Lizuo","year":"2022","unstructured":"Lizuo Liu, Kamaljyoti Nath, and Wei Cai. 2022. A causality-DeepONet for causal responses of linear dynamical systems. arXiv preprint arXiv:2209.08397 (2022).","journal-title":"arXiv preprint arXiv:2209.08397"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2020.3011449"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(94)90473-1"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/LMWC.2013.2290218"},{"key":"e_1_3_1_27_2","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"Kendall Alex","year":"2018","unstructured":"Alex Kendall, Yarin Gal, and Roberto Cipolla. 2018. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201918). 7482\u20137491.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201918)"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070203"},{"key":"e_1_3_1_29_2","article-title":"Multi-task learning with deep neural networks: A survey","author":"Crawshaw Michael","year":"2020","unstructured":"Michael Crawshaw. 2020. Multi-task learning with deep neural networks: A survey. arXiv preprint arXiv:2009.09796 (2020).","journal-title":"arXiv preprint arXiv:2009.09796"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/MMM.2021.3095990"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD45719.2019.8942109"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ECTC.2017.187"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.5555\/2746451"}],"container-title":["ACM Transactions on Design Automation of Electronic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663476","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3663476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:56:46Z","timestamp":1750291006000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,4]]},"references-count":32,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3663476"],"URL":"https:\/\/doi.org\/10.1145\/3663476","relation":{},"ISSN":["1084-4309","1557-7309"],"issn-type":[{"value":"1084-4309","type":"print"},{"value":"1557-7309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,4]]},"assertion":[{"value":"2023-09-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}