{"id":40028,"date":"2026-10-19T09:51:50","date_gmt":"2026-10-19T07:51:50","guid":{"rendered":"https:\/\/ggba.swiss\/?p=40028"},"modified":"2026-10-19T09:51:51","modified_gmt":"2026-10-19T07:51:51","slug":"cern-and-mistral-ai-join-forces-to-develop-ai-foundation-models-for-science","status":"publish","type":"post","link":"https:\/\/ggba.swiss\/en\/cern-and-mistral-ai-join-forces-to-develop-ai-foundation-models-for-science\/","title":{"rendered":"CERN and Mistral AI join forces to develop AI foundation models for science"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/home.cern\/\">CERN<\/a> and <a href=\"https:\/\/mistral.ai\/\">Mistral AI<\/a> have signed a statement of intent for a long-term strategic partnership on foundation models for scientific discovery. CERN contributes its expertise in fundamental physics, scientific data and large-scale computing, while Mistral AI brings large-scale model training. The initial focus is high-energy physics, but the work could also inform AI approaches for the wider scientific community.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">A foundation model for collider physics<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The central goal is a single pretrained model, described as the first foundation model for collider physics: a &#8220;Large Physics Model&#8221;. It would be trained on billions of simulated proton-proton collisions at the Large Hadron Collider (LHC) and support several tasks, including reconstructing particle collisions, identifying particles, flagging unexpected signals and evaluating future detector designs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Today, reconstruction relies on a chain of specialized algorithms that progressively convert detector signals into physics objects. Machine learning already improves individual steps of this chain, and a foundation model could learn across all of it within a single model.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The volume of data involved is considerable. CERN has stored about one exabyte (one million terabytes) of LHC experimental data to date, and the High-Luminosity LHC (HL-LHC), expected to start operating around 2030, will increase the number of collisions tenfold.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Shared AI capabilities for physics<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Enrica Porcari, CERN Chief Information Officer, says high-energy physics offers &#8220;a unique environment in which artificial intelligence can be developed and tested&#8221; with strong rigor, and that the partnership aims to create AI capabilities that the wider physics community can share. Johannes Brandstetter, Vice-President of AI for Engineering at Mistral AI, says the company is proud to work with CERN to explore what large-scale AI models can offer to particle physics, with the potential for new scientific discoveries.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>CERN in Geneva and AI developer Mistral AI have signed a statement of intent for a long-term strategic partnership on foundation models for scientific discovery. The first goal is a &#8220;Large Physics Model&#8221; for high-energy physics.<\/p>\n","protected":false},"author":6,"featured_media":40029,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"mc4wp_mailchimp_campaign":[],"footnotes":""},"categories":[839],"tags":[893,907,886,887],"class_list":["post-40028","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-en","tag-artificial-intelligence","tag-engineering","tag-rd","tag-software"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>CERN and Mistral AI team up on AI for physics | GGBa<\/title>\n<meta name=\"description\" content=\"CERN and Mistral AI have signed an intent to build a foundation model for collider physics, trained on billions of simulated LHC collisions.\" \/>\n<meta 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