{"id":3469,"date":"2026-01-12T14:05:42","date_gmt":"2026-01-12T05:05:42","guid":{"rendered":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/?post_type=seminar&#038;p=3469"},"modified":"2026-01-12T14:05:42","modified_gmt":"2026-01-12T05:05:42","slug":"generative-ai-in-cosmology","status":"publish","type":"seminar","link":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/3469\/","title":{"rendered":"Generative AI in Cosmology"},"content":{"rendered":"<p>Increasing data volumes, pushing to non-linear scales, create opportunities for machine learning in cosmology. One primary challenges is the inverse problem implicitly defined through simulations. Neural simulation-based inference is increasingly being recognized as a tool.<\/p>\n<p>I will review this technique and present some work both on observational data as well as on methodological development, specifically multi-fidelity inference. In the second part of the talk, I will present recent work on probabilistic identification of cosmic voids.<\/p>\n","protected":false},"featured_media":0,"template":"","tags":[],"seminar_category":[154],"acf":{"s_now_accepting":true,"s_date_order":"2026-01-13 15:00:00","s_date_end":"2026-01-13 16:00:00","s_date_text":"15:00 - 16:00","s_text":"Leander Thiele (Kavli IPMU)","s_place":"ES635 + Zoom","s_place_other":"","s_categoryother":"","s_poster":"","s_poster2":"","s_slide":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Generative AI in Cosmology - KMI - Nagoya University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/3469\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Generative AI in Cosmology - KMI - Nagoya University\" \/>\n<meta property=\"og:description\" content=\"Increasing data volumes, pushing to non-linear scales, create opportunities for machine learning in cosmology. 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