Future of Artificial Intelligence for Science in Japan 2024 Community Report
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
[2608.27807] Future of Artificial Intelligence for Science in Japan 2024 Community Report Skip to main content Search Submit Donate Log in High Energy Physics - Phenomenology arXiv:2608.27807 (hep-ph) [Submitted on 28 Aug 2026 ( v1 ), last revised 18 Sep 2026 (this version, v2)] Title: Future of Artificial Intelligence for Science in Japan 2024 Community Report Authors: Yoshitaka Itow , Jia Liu , Hirokazu Maesaka , Vinicius Mikuni , Nhat-Minh Nguyen , Hironao Miyatake , Atsushi J. Nishizawa , Patrick de Perio , Daniel Ratner , Kazuhiro Terao , Leander Thiele , Omar Alterkait , Francois Drielsma , Rocio Garcia , Masako Iwasaki , Ahsani Hafizhu Shali , Federica Tarsitano , Takahiro Terada , Junjie Xia View PDF HTML (experimental) Abstract: This white paper summarizes scientific challenges and AI/ML research opportunities identified through the FAIRS Japan 2024 unconference process. The discussion focuses on three major physics domains: accelerator physics, cosmology and astrophysics, and neutrino physics. Although each domain has distinct scientific goals and experimental constraints, several common technical themes emerge: high-dimensional reconstruction, fast and accurate simulation, uncertainty propagation, simulation-to-data mismatch, anomaly detection, real-time decision-making, and shared infrastructure. Subjects: High Energy Physics - Phenomenology (hep-ph) ; Instrumentation and Methods for Astrophysics (astro-ph.IM); High Energy Physics - Experiment (hep-ex); Accelerator Physics (physics.acc-ph) Report number: IPMU26-0035 Cite as: arXiv:2608.27807 [hep-ph] (or arXiv:2608.27807v2 [hep-ph] for this version) https://doi.org/10.48550/arXiv.2608.27807 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nhat-Minh Nguyen [ view email ] [v1] Fri, 28 Aug 2026 00:48:49 UTC (35 KB) [v2] Fri, 18 Sep 2026 08:53:42 UTC (35 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: hep-ph < prev | next > new | recent | 2026-08 Change to browse by: astro-ph astro-ph.IM hep-ex physics physics.acc-ph References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation 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? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) 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? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from
Related documents
- Future of Artificial Intelligence for Science in Japan 2024 Community Report#779722
- The promise of artificial intelligence in chemical engineering: Is it here, finally?#412779
- Physics-Informed Neural Networks in Aerospace: A Structured Taxonomy with Literature Review#698972
- SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks#303001
- Toward a New Vision for Humanity#692802
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive