{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/latent-space-physics-towards-learning-the","title":"Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow","arxiv_id":"1802.10123","date":"2018-02-27","proceeding":null,"authors":["Steffen Wiewel","Moritz Becher","Nils Thuerey"],"abstract":"We propose a method for the data-driven inference of temporal evolutions of\nphysical functions with deep learning. More specifically, we target fluid\nflows, i.e. Navier-Stokes problems, and we propose a novel LSTM-based approach\nto predict the changes of pressure fields over time. The central challenge in\nthis context is the high dimensionality of Eulerian space-time data sets. We\ndemonstrate for the first time that dense 3D+time functions of physics system\ncan be predicted within the latent spaces of neural networks, and we arrive at\na neural-network based simulation algorithm with significant practical\nspeed-ups. We highlight the capabilities of our method with a series of complex\nliquid simulations, and with a set of single-phase buoyancy simulations. With a\nset of trained networks, our method is more than two orders of magnitudes\nfaster than a traditional pressure solver. Additionally, we present and discuss\na series of detailed evaluations for the different components of our algorithm.","url_abs":"http://arxiv.org/abs/1802.10123v3","url_pdf":"http://arxiv.org/pdf/1802.10123v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"latent-space-physics-towards-learning-the","repo_url":"https://github.com/wiewel/LatentSpacePhysics","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"latent-space-physics-towards-learning-the","repo_url":"https://github.com/lij131/LatentSpacePhysics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10123","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}