PhysMiner：用于自动流动分量分析的智能体 AI 框架

Physics > Fluid Dynamics arXiv:2607.04009 (physics) [Submitted on 4 Jul 2026 (v1), last revised 24 Sep 2026 (this version, v2)] Title:PhysMiner: An Agentic AI Framework for Automated Flow Component Analysis Authors:Jiawei Chen, Han Gao, Ping He View a PDF of the paper titled PhysMiner: An Agentic AI Framework for Automated Flow Component Analysis, by Jiawei Chen and 2 other authors View PDF HTML (experimental) Abstract:Uncovering the physical mechanisms of turbulent flows remains a fundamental challenge in fluid mechanics. In particular, conventional velocity-gradient analysis methods suffer from shear contamination, which hinders accurate identification of the dominant physical mechanisms. This study presents PhysMiner, an automated framework integrating the triple decomposition method of the velocity gradient tensor with large language model-driven reasoning for turbulence-physics discovery. The triple decomposition module automatically decomposes flow fields into rigid rotation, pure shearing, and normal straining components, enabling statistical analysis, contour visualization, vortex-line extraction, and threshold-insensitive vortex identification while eliminating shear contamination. These automated capabilities are validated across five benchmarks, ranging from canonical configurations to complex engineering flows. A discover-physics agent combines flow statistics, spatial structures, and literature-derived knowledge to perform pattern recognition and physical inference, while a review Agent iteratively validates physical consistency to ensure reliable conclusions. A continuously evolving Triple Decomposition Library accumulates statistical knowledge from successfully analyzed flows, enabling cross-case comparison and progressive enhancement of inductive capability. The complete PhysMiner pipeline is validated end-to-end on the periodic hill flow, where the framework autonomously generates turbulence modeling recommendations and derives an improved subgrid-scale model with superior Reynolds-stress predictions. PhysMiner is open to the public and establishes a foundation for long-term collaborative advancement in automated turbulence-physics discovery. Comments: Subjects: Fluid Dynamics (physics.flu-dyn) Cite as: arXiv:2607.04009 [physics.flu-dyn] (or arXiv:2607.04009v2 [physics.flu-dyn] for this version) https://doi.org/10.48550/arXiv.2607.04009 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jiawei Chen [view email] [v1] Sat, 4 Jul 2026 20:08:55 UTC (30,816 KB) [v2] Thu, 24 Sep 2026 17:10:12 UTC (41,026 KB) Full-text links: Access Paper: View a PDF of the paper titled PhysMiner: An Agentic AI Framework for Automated Flow Component Analysis, by Jiawei Chen and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: physics.flu-dyn new | recent | 2026-07 Change to browse by: physics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark 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?) 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?)