{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:40:06Z","timestamp":1778168406302,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["Research Training Group GRK 2153: Energy Status Data ? Informatics Methods for its Collection, Analysis and Exploitation"],"award-info":[{"award-number":["Research Training Group GRK 2153: Energy Status Data ? Informatics Methods for its Collection, Analysis and Exploitation"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,4]]},"DOI":"10.1145\/3632775.3661967","type":"proceedings-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T15:31:37Z","timestamp":1720539097000},"page":"279-290","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3242-9113","authenticated-orcid":false,"given":"Pawel","family":"Bielski","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4722-2873","authenticated-orcid":false,"given":"Aleksandr","family":"Eismont","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0301-2798","authenticated-orcid":false,"given":"Jakob","family":"Bach","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5347-0493","authenticated-orcid":false,"given":"Florian","family":"Leiser","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5571-1729","authenticated-orcid":false,"given":"Dustin","family":"Kottonau","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1706-1913","authenticated-orcid":false,"given":"Klemens","family":"B\u00f6hm","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg\u00a0S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dan Mane Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viegas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. http:\/\/arxiv.org\/abs\/1603.04467 arXiv:1603.04467 [cs]."},{"key":"e_1_3_2_1_2_1","unstructured":"Yaser Abu-Mostafa. 1992. A Method for Learning From Hints. In Advances in Neural Information Processing Systems Vol.\u00a05. Morgan-Kaufmann. https:\/\/proceedings.neurips.cc\/paper\/1992\/hash\/7750ca3559e5b8e1f44210283368fc16-Abstract.html"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/en15218084"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.126.098302"},{"key":"e_1_3_2_1_5_1","volume-title":"Modeling of gas turbines and steam turbines in combined cycle power plants. CIGRE Technical Brochure 238","author":"CIGRE","year":"2003","unstructured":"CIGRE Task\u00a0Force C4.02.25. 2003. Modeling of gas turbines and steam turbines in combined cycle power plants. CIGRE Technical Brochure 238 (2003)."},{"key":"e_1_3_2_1_6_1","article-title":"Dynamic models for combined cycle plants in power system studies","volume":"9","author":"De\u00a0Mello P.","year":"1994","unstructured":"F.\u00a0P. De\u00a0Mello and D.\u00a0J. Ahner. 1994. Dynamic models for combined cycle plants in power system studies. IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers);(United States) 9, 3 (1994). https:\/\/www.osti.gov\/biblio\/6912824","journal-title":"IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers);(United States)"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/en16020704"},{"key":"e_1_3_2_1_8_1","unstructured":"Franck Djeumou Cyrus Neary Eric Goubault Sylvie Putot and Ufuk Topcu. 2022. Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling. http:\/\/arxiv.org\/abs\/2109.06407"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3530911"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/en10010011"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2015.07.066"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.63"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447814"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2720168"},{"key":"e_1_3_2_1_15_1","volume-title":"Physics-guided neural networks (pgnn): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431 2","author":"Karpatne Anuj","year":"2017","unstructured":"Anuj Karpatne, William Watkins, Jordan Read, and Vipin Kumar. 2017. Physics-guided neural networks (pgnn): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431 2 (2017). https:\/\/arxiv.org\/abs\/1710.11431"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0093"},{"key":"e_1_3_2_1_17_1","volume-title":"Advances in Neural Information Processing Systems, Vol.\u00a032. Curran Associates","author":"Kolter Zico","year":"2019","unstructured":"J.\u00a0Zico Kolter and Gaurav Manek. 2019. Learning Stable Deep Dynamics Models. In Advances in Neural Information Processing Systems, Vol.\u00a032. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/0a4bbceda17a6253386bc9eb45240e25-Abstract.html"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5445\/IR\/1000156019\/v2"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8621955"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/en17020352"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.07.023"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.3227494"},{"key":"e_1_3_2_1_23_1","article-title":"Parameter Estimation and Dynamic Simulation Of Gas Turbine Model In Combined Cycle Power Plants Based On Actual Operational Data","author":"Shalan Emam","year":"2011","unstructured":"Emam Shalan, Mohamed Moustafa\u00a0Hassan, and ABG Bahgat. 2011. Parameter Estimation and Dynamic Simulation Of Gas Turbine Model In Combined Cycle Power Plants Based On Actual Operational Data. Journal of American Science 7 (Jan. 2011). https:\/\/www.researchgate.net\/publication\/239523797_Parameter_Estimation_and_Dynamic_Simulation_Of_Gas_Turbine_Model_In_Combined_Cycle_Power_Plants_Based_On_Actual_Operational_Data","journal-title":"Journal of American Science 7"},{"key":"e_1_3_2_1_24_1","unstructured":"Patrice Simard Bernard Victorri Yann LeCun and John Denker. 1991. Tangent Prop - A formalism for specifying selected invariances in an adaptive network. In Advances in Neural Information Processing Systems Vol.\u00a04. Morgan-Kaufmann. https:\/\/proceedings.neurips.cc\/paper\/1991\/hash\/65658fde58ab3c2b6e5132a39fae7cb9-Abstract.html"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121130"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2009.2021231"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3079836"},{"key":"e_1_3_2_1_28_1","unstructured":"Rui Wang and Rose Yu. 2022. Physics-Guided Deep Learning for Dynamical Systems: A Survey. http:\/\/arxiv.org\/abs\/2107.01272"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514228"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ac3ae5"}],"event":{"name":"e-Energy '24: The 15th ACM International Conference on Future and Sustainable Energy Systems","location":"Singapore Singapore","acronym":"e-Energy '24"},"container-title":["The 15th ACM International Conference on Future and Sustainable Energy Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632775.3661967","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3632775.3661967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:36:30Z","timestamp":1755884190000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632775.3661967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":30,"alternative-id":["10.1145\/3632775.3661967","10.1145\/3632775"],"URL":"https:\/\/doi.org\/10.1145\/3632775.3661967","relation":{},"subject":[],"published":{"date-parts":[[2024,5,31]]},"assertion":[{"value":"2024-05-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}