{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T02:21:46Z","timestamp":1730254906756,"version":"3.28.0"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,10]]},"DOI":"10.1109\/icpr48806.2021.9412335","type":"proceedings-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T02:15:54Z","timestamp":1620267354000},"page":"5091-5098","source":"Crossref","is-referenced-by-count":0,"title":["Transferable Model for Shape Optimization subject to Physical Constraints"],"prefix":"10.1109","author":[{"given":"Lukas","family":"Harsch","sequence":"first","affiliation":[]},{"given":"Johannes","family":"Burgbacher","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Riedelbauch","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Lagrangian Fluid Simulation with Continuous Convolutions","author":"ummenhofer","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939738"},{"journal-title":"A comprehensive survey on graph neural networks","year":"2019","author":"wu","key":"ref12"},{"key":"ref13","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","author":"ronneberger","year":"2015","journal-title":"CoRR vol abs\/1505 04597"},{"key":"ref14","article-title":"Spa-tial Transformer Networks","author":"jaderberg","year":"2015","journal-title":"CoRR vol abs\/1506 02025"},{"key":"ref15","first-page":"2672","article-title":"Generative Adversarial Nets","author":"goodfellow","year":"2014","journal-title":"Advances in Neural Information Processing Systems 27"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2015.01.030"},{"key":"ref17","article-title":"Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures","volume":"5","author":"liu","year":"2017","journal-title":"ACS Photonics"},{"journal-title":"Hidden Fluid Mechanics A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data","year":"2018","author":"raissi","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13619"},{"key":"ref4","article-title":"Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations","author":"thuerey","year":"2018","journal-title":"CoRR vol abs\/1810 08217"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.08.029"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201304"},{"key":"ref5","first-page":"3424","article-title":"Accelerating Eulerian Fluid Simulation with Convolutional Networks","author":"tompson","year":"0","journal-title":"Proceedings of the 34th International Conference on Machine Learning - Volume 70 ser ICML17 JMLR org"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012784129883"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/72.712178"},{"key":"ref2","first-page":"25:1","article-title":"Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations","volume":"19","author":"raissi","year":"2018","journal-title":"J Mach Learn Res"},{"key":"ref9","article-title":"SPNets: Differentiable Fluid Dynamics for Deep Neural Networks","author":"schenck","year":"2018","journal-title":"CoRR vol abs\/1806 06094"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2928212"},{"key":"ref20","article-title":"Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow","volume":"38","author":"wiewel","year":"2018","journal-title":"Computer Graphics Forum"},{"key":"ref22","article-title":"The FEniCS Project Version 1.5","volume":"3","author":"alnaes","year":"2015","journal-title":"Archive of Numerical Software"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.2514\/1.J057894"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/34.24792"},{"journal-title":"Adam A method for stochastic optimization","year":"2014","author":"kingma","key":"ref23"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-47977-5_2"}],"event":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2021,1,10]]},"location":"Milan, Italy","end":{"date-parts":[[2021,1,15]]}},"container-title":["2020 25th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9411940\/9411911\/09412335.pdf?arnumber=9412335","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:40:46Z","timestamp":1652197246000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412335\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412335","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}