{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:01:37Z","timestamp":1750309297118,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["2324735"],"award-info":[{"award-number":["2324735"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["FA8750-20-C-0537"],"award-info":[{"award-number":["FA8750-20-C-0537"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3659914.3659919","type":"proceedings-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T14:13:51Z","timestamp":1715782431000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward Improving Boussinesq Flow Simulations by Learning with Compressible Flow"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7680-893X","authenticated-orcid":false,"given":"Nurshat","family":"Mangnike","sequence":"first","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4950-5533","authenticated-orcid":false,"given":"David","family":"Hyde","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America"}]}],"member":"320","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"https:\/\/doc.comsol.com\/6.1\/doc\/com.comsol.help.comsol\/comsol_ref_solver.35.129.html [Online","author":"Solver Time-Dependent","year":"2024","unstructured":"2023. Time-Dependent Solver. https:\/\/doc.comsol.com\/6.1\/doc\/com.comsol.help.comsol\/comsol_ref_solver.35.129.html [Online; accessed 21-February-2024]."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechrescom.2022.103939"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/physics5010022"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2017.06.024"},{"key":"e_1_3_2_1_5_1","volume-title":"Thorne","author":"Blandford Roger D.","year":"2013","unstructured":"Roger D. Blandford and Kip S. Thorne. 2013. Applications of Classical Physics. http:\/\/www.pmaweb.caltech.edu\/Courses\/ph136\/yr2012\/"},{"key":"e_1_3_2_1_6_1","unstructured":"Joseph Boussinesq. 1903. Th\u00e9orie analytique de la chaleur mise en harmonic avec la thermodynamique et avec la th\u00e9orie m\u00e9canique de la lumi\u00e8re: Refroidissement et \u00e9chauffement par rayonnement conductibilit\u00e9 des tiges lames et masses cristallines courants de convection th\u00e9orie m\u00e9canique de la lumi\u00e8re. Vol. 2. Gauthier-Villars Paris France."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-fluid-010719-060214"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4050542"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-022-01939-z"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.38.3.235"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/fld.1650030305"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.316"},{"key":"e_1_3_2_1_13_1","volume-title":"Pande","author":"Farimani Amir Barati","year":"2017","unstructured":"Amir Barati Farimani, Joseph Gomes, and Vijay S. Pande. 2017. Deep learning the physics of transport phenomena. arXiv preprint arXiv:1709.02432 (2017). https:\/\/arxiv.org\/pdf\/1709.02432.pdf"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1967-0225494-5"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.109099"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.05.031"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/0017-9310(76)90168-X"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939738"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/19942060.2022.2030802"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00044"},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"15736","author":"Kaneda Ayano","year":"2023","unstructured":"Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Alicia Trevino Kala, David Hyde, and Joseph Teran. 2023. A Deep Conjugate Direction Method for Iteratively Solving Linear Systems. In Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 202), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 15720--15736."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5"},{"key":"e_1_3_2_1_23_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). https:\/\/arxiv.org\/pdf\/1412.6980.pdf"},{"key":"e_1_3_2_1_24_1","volume-title":"Mahoney","author":"Krishnapriyan Aditi","year":"2021","unstructured":"Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, and Michael W. Mahoney. 2021. Characterizing possible failure modes in physics-informed neural networks. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., Red Hook, New York, 26548--26560."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2023.0058"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1137\/21M1397908"},{"key":"e_1_3_2_1_27_1","volume-title":"Blaschke","author":"Luna Kevin","year":"2021","unstructured":"Kevin Luna, Katherine Klymko, and Johannes P. Blaschke. 2021. Accelerating GMRES with deep learning in real-time. arXiv preprint arXiv:2103.10975 (2021). https:\/\/arxiv.org\/pdf\/2103.10975.pdf"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.icheatmasstransfer.2021.105316"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.2430726"},{"key":"e_1_3_2_1_31_1","unstructured":"Jinho Park. 2019. Jinh0park\/Pytorch-SSIM-3D: Pytorch structural similarity (SSIM) loss for 3D images. https:\/\/github.com\/jinh0park\/pytorch-ssim-3D"},{"key":"e_1_3_2_1_32_1","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga et al. 2019. PyTorch: An imperative style high-performance deep learning library. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2016.02.010"},{"key":"e_1_3_2_1_34_1","volume-title":"Physics informed deep learning (part I): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561","author":"Raissi Maziar","year":"2017","unstructured":"Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. 2017. Physics informed deep learning (part I): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561 (2017). https:\/\/arxiv.org\/pdf\/1711.10561.pdf"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.coastaleng.2021.104021"},{"key":"e_1_3_2_1_38_1","volume-title":"Joe Gomes, Peter Eastman, and Vijay Pande.","author":"Sharma Rishi","year":"2018","unstructured":"Rishi Sharma, Amir Barati Farimani, Joe Gomes, Peter Eastman, and Vijay Pande. 2018. Weakly-supervised deep learning of heat transport via physics informed loss. arXiv preprint arXiv:1807.11374 (2018). https:\/\/arxiv.org\/pdf\/1807.11374.pdf"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3086020"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.2514\/1.J058291"},{"key":"e_1_3_2_1_41_1","volume-title":"International Conference on Machine Learning. PMLR, 3424--3433","author":"Tompson Jonathan","year":"2017","unstructured":"Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin. 2017. Accelerating Eulerian fluid simulation with convolutional networks. In International Conference on Machine Learning. PMLR, 3424--3433. http:\/\/proceedings.mlr.press\/v70\/tompson17a\/tompson17a.pdf"},{"key":"e_1_3_2_1_42_1","first-page":"6111","article-title":"Solver-in-the-loop: Learning from differentiable physics to interact with iterative PDE-solvers","volume":"33","author":"Um Kiwon","year":"2020","unstructured":"Kiwon Um, Robert Brand, Yun Raymond Fei, Philipp Holl, and Nils Thuerey. 2020. Solver-in-the-loop: Learning from differentiable physics to interact with iterative PDE-solvers. Advances in Neural Information Processing Systems 33 (2020), 6111--6122.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2016.7888195"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1108\/09615530110389117"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_3_2_1_46_1","volume-title":"Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919 1, 1","author":"Willard Jared","year":"2020","unstructured":"Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2020. Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919 1, 1 (2020), 1--34. https:\/\/arxiv.org\/pdf\/2003.04919.pdf"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108900"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/cav.1695"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1631-0721(03)00120-7"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2011.11.020"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00466-020-01952-9"}],"event":{"name":"PASC '24: Platform for Advanced Scientific Computing Conference","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","ETH Zurich \/ CSCS"],"location":"Zurich Switzerland","acronym":"PASC '24"},"container-title":["Proceedings of the Platform for Advanced Scientific Computing Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3659914.3659919","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3659914.3659919","content-type":"text\/html","content-version":"vor","intended-application":"syndication"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:37Z","timestamp":1750291417000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3659914.3659919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":50,"alternative-id":["10.1145\/3659914.3659919","10.1145\/3659914"],"URL":"https:\/\/doi.org\/10.1145\/3659914.3659919","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2024-06-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}