https://arxiv.org/abs/2410.20203 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > physics > arXiv:2410.20203 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Physics > Fluid Dynamics arXiv:2410.20203 (physics) [Submitted on 26 Oct 2024 (v1), last revised 2 Nov 2024 (this version, v2)] Title:Physics-informed Shadowgraph Network: An End-to-end Density Field Reconstruction Method Authors:Xutun Wang, Yuchen Zhang, Zidong Li, Haocheng Wen, Bing Wang View a PDF of the paper titled Physics-informed Shadowgraph Network: An End-to-end Density Field Reconstruction Method, by Xutun Wang and 4 other authors View PDF Abstract:This study presents a novel approach for quantificationally reconstructing density fields from shadowgraph images using physics-informed neural networks Subjects: Fluid Dynamics (physics.flu-dyn); Artificial Intelligence (cs.AI) Cite as: arXiv:2410.20203 [physics.flu-dyn] (or arXiv:2410.20203v2 [physics.flu-dyn] for this version) https://doi.org/10.48550/arXiv.2410.20203 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xutun Wang Wang [view email] [v1] Sat, 26 Oct 2024 15:28:15 UTC (1,397 KB) [v2] Sat, 2 Nov 2024 12:45:32 UTC (18,767 KB) Full-text links: Access Paper: View a PDF of the paper titled Physics-informed Shadowgraph Network: An End-to-end Density Field Reconstruction Method, by Xutun Wang and 4 other authors * View PDF * Other Formats license icon view license Current browse context: physics.flu-dyn < prev | next > new | recent | 2024-10 Change to browse by: cs cs.AI physics References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... BibTeX formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Reddit logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) [ ] scite.ai Toggle scite Smart Citations (What are Smart Citations?) ( ) Code, Data, Media Code, Data and Media Associated with this Article [ ] alphaXiv Toggle alphaXiv (What is alphaXiv?) [ ] Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) [ ] DagsHub Toggle DagsHub (What is DagsHub?) [ ] GotitPub Toggle Gotit.pub (What is GotitPub?) [ ] Huggingface Toggle Hugging Face (What is Huggingface?) [ ] Links to Code Toggle Papers with Code (What is Papers with Code?) [ ] ScienceCast Toggle ScienceCast (What is ScienceCast?) ( ) Demos Demos [ ] Replicate Toggle Replicate (What is Replicate?) [ ] Spaces Toggle Hugging Face Spaces (What is Spaces?) [ ] Spaces Toggle TXYZ.AI (What is TXYZ.AI?) ( ) Related Papers Recommenders and Search Tools [ ] Link to Influence Flower Influence Flower (What are Influence Flowers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) * Author * Venue * Institution * Topic ( ) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack