{"id":3443,"date":"2025-12-02T11:19:10","date_gmt":"2025-12-02T02:19:10","guid":{"rendered":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/?post_type=seminar&p=3443"},"modified":"2025-12-02T11:19:10","modified_gmt":"2025-12-02T02:19:10","slug":"physics-driven-learning-for-solving-inverse-problems-in-qcd-physics","status":"publish","type":"seminar","link":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/3443\/","title":{"rendered":"Physics-Driven Learning for Solving Inverse Problems in QCD Physics"},"content":{"rendered":"

Discovery in the physical sciences relies on inverse modeling of observations. The combination of deep learning and physics-driven designs is reshaping how we solve inverse problems for extracting physical properties from data. This is particularly relevant for quantum chromodynamics (QCD), where non-trivial symmetries make both data interpretation and computation challenging.
\nIn this talk, I will present physics-driven learning from a probabilistic perspective, with a focus on applications in QCD physics. Examples include learning spectral functions and hadron forces from lattice QCD data, reconstructing hadron emission sources from Femtoscopy, and extracting the equation of state from neutron-star observations. If time permits, I will also introduce the physics of diffusion models and discuss physics-driven designs that enable expandable and reliable sampling for accelerating simulations.<\/p>\n

\n

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\u3053\u306e\u30bb\u30df\u30ca\u30fc\u3067\u306f\u3001\u78ba\u7387\u7684\u306a\u89b3\u70b9\u304b\u3089\u7269\u7406\u99c6\u52d5\u578b\u5b66\u7fd2\u3092\u7d39\u4ecb\u3057\u3001QCD\u7269\u7406\u3078\u306e\u5fdc\u7528\u306b\u7126\u70b9\u3092\u5f53\u3066\u307e\u3059\u3002\u5177\u4f53\u4f8b\u3068\u3057\u3066\u3001\u683c\u5b50QCD\u30c7\u30fc\u30bf\u304b\u3089\u30b9\u30da\u30af\u30c8\u30eb\u95a2\u6570\u3084\u30cf\u30c9\u30ed\u30f3\u9593\u529b\u3092\u5b66\u7fd2\u3059\u308b\u65b9\u6cd5\u3001\u30d5\u30a7\u30e0\u30c8\u30b9\u30b3\u30d4\u30fc\u306b\u3088\u308b\u30cf\u30c9\u30ed\u30f3\u653e\u51fa\u6e90\u306e\u518d\u69cb\u7bc9\u3001\u305d\u3057\u3066\u4e2d\u6027\u5b50\u661f\u306e\u89b3\u6e2c\u304b\u3089\u72b6\u614b\u65b9\u7a0b\u5f0f\u3092\u62bd\u51fa\u3059\u308b\u624b\u6cd5\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002\u6642\u9593\u304c\u8a31\u305b\u3070\u3001\u62e1\u6563\u30e2\u30c7\u30eb\u306e\u7269\u7406\u306b\u3064\u3044\u3066\u3082\u7d39\u4ecb\u3057\u3001\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u52a0\u901f\u3059\u308b\u305f\u3081\u306e\u62e1\u5f35\u6027\u3068\u4fe1\u983c\u6027\u3092\u5099\u3048\u305f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u8a2d\u8a08\u306b\u3064\u3044\u3066\u8b70\u8ad6\u3057\u307e\u3059\u3002<\/p>\n","protected":false},"featured_media":0,"template":"","tags":[],"seminar_category":[154],"acf":{"s_now_accepting":true,"s_date_order":"2025-12-19 17:00:00","s_date_end":"2025-12-19 18:00:00","s_date_text":"17:00 - 18:00","s_text":"Lingxiao Wang (RIKEN)","s_place":"ES635 + Zoom","s_place_other":"","s_categoryother":"","s_poster":"","s_poster2":"","s_slide":""},"yoast_head":"\nPhysics-Driven Learning for Solving Inverse Problems in QCD Physics - 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\/3443\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Physics-Driven Learning for Solving Inverse Problems in QCD Physics - KMI - Nagoya University\" \/>\n<meta property=\"og:description\" content=\"Discovery in the physical sciences relies on inverse modeling of observations. 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