{"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/190506327","title":"A data-driven proxy to Stoke's flow in porous media","arxiv_id":"1905.06327","date":"2019-04-25","proceeding":null,"authors":["Ali Takbiri-Borujeni","Hadi Kazemi","Nasser Nasrabadi"],"abstract":"The objective for this work is to develop a data-driven proxy to\nhigh-fidelity numerical flow simulations using digital images. The proposed\nmodel can capture the flow field and permeability in a large verity of digital\nporous media based on solid grain geometry and pore size distribution by\ndetailed analyses of the local pore geometry and the local flow fields. To\ndevelop the model, the detailed pore space geometry and simulation runs data\nfrom 3500 two-dimensional high-fidelity Lattice Boltzmann simulation runs are\nused to train and to predict the solutions with a high accuracy in much less\ncomputational time. The proposed methodology harness the enormous amount of\ngenerated data from high-fidelity flow simulations to decode the often\nunder-utilized patterns in simulations and to accurately predict solutions to\nnew cases. The developed model can truly capture the physics of the problem and\nenhance prediction capabilities of the simulations at a much lower cost. These\npredictive models, in essence, do not spatio-temporally reduce the order of the\nproblem. They, however, possess the same numerical resolutions as their Lattice\nBoltzmann simulations equivalents do with the great advantage that their\nsolutions can be achieved by significant reduction in computational costs\n(speed and memory).","url_abs":"http://arxiv.org/abs/1905.06327v1","url_pdf":"http://arxiv.org/pdf/1905.06327v1.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":"190506327","repo_url":"https://github.com/Ali805509/A-data-driven-proxy-to-Stokes-flow-in-porous-media","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}