{"id":51456,"date":"2024-07-14T22:15:35","date_gmt":"2024-07-14T14:15:35","guid":{"rendered":"https:\/\/swarma.org\/?p=51456"},"modified":"2024-07-14T22:15:35","modified_gmt":"2024-07-14T14:15:35","slug":"%e4%bb%8e%e9%ab%98%e9%98%b6%e7%9b%b8%e4%ba%92%e4%bd%9c%e7%94%a8%e5%88%b0%e7%a5%9e%e7%bb%8f%e7%ae%97%e5%ad%90%e6%a8%a1%e5%9e%8b%ef%bc%9a%e5%90%af%e5%8f%91%e6%9b%b4%e5%a5%bd%e7%9a%84ai-%e5%91%a8","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51456","title":{"rendered":"\u4ece\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528\u5230\u795e\u7ecf\u7b97\u5b50\u6a21\u578b\uff1a\u542f\u53d1\u66f4\u597d\u7684AI | \u5468\u4e00\u76f4\u64ad\u00b7AI by Complexity\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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\u5173\u952e\u8bcd\uff1a<\/strong><\/span>\u91cd\u6574\u5316\u7fa4\u3001\u8def\u5f84\u79ef\u5206\u3001\u5c3a\u5ea6\u4e0d\u53d8\u6027\u3001\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528<\/strong><\/span><\/section>\n

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      \u4e0d\u53ef\u5206\u89e3\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528<\/p>\n<\/li>\n<\/ol>\n

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      \u5355\u7eaf\u5f62\u8def\u5f84\u79ef\u5206<\/p>\n<\/li>\n

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      \u5355\u7eaf\u5f62\u91cd\u6574\u5316\u7fa4<\/p>\n<\/li>\n

    5. \n
      \u5355\u7eaf\u5f62\u91cd\u6574\u5316\u7fa4\u5206\u6790\u5e94\u7528<\/section>\n<\/li>\n<\/ol>\n

      <\/strong><\/span><\/section>\n
      \u53c2\u8003\u6587\u732e<\/strong><\/span><\/section>\n
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        Cheng, A., Xu, Y., Sun, P., & Tian, Y. (2024). Simplex path integral and simplex renormalization group for high-order interactions. arXiv preprint arXiv:2305.01895. Accepted by Reports on Progress in Physics<\/span><\/p>\n<\/li>\n

      2. \n

        Lucas M, Cencetti G, Battiston F. Multiorder Laplacian for synchronization in higher-order networks[J]. Physical Review Research, 2020, 2(3): 033410.<\/span><\/p>\n<\/li>\n

      3. \n

        Villegas P, Gili T, Caldarelli G, et al. Laplacian renormalization group for heterogeneous networks[J]. Nature Physics, 2023, 19(3): 445-450.<\/span><\/p>\n<\/li>\n

      4. \n

        Bradde S, Bialek W. Pca meets rg[J]. Journal of statistical physics, 2017, 167: 462-475.<\/span><\/p>\n<\/li>\n

      5. \n
        Meshulam L, Gauthier J L, Brody C D, et al. Coarse\u2013graining and hints of scaling in a population of 1000+ neurons. arXiv 2018[J]. arXiv preprint arXiv:1812.11904.<\/span><\/section>\n<\/li>\n<\/ol>\n

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        \u4e3b\u9898\u4e8c\uff1aKoopman\u795e\u7ecf\u7b97\u5b50\u6c42\u89e3\u504f\u5fae\u5206\u65b9\u7a0b<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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        \u5173\u952e\u8bcd\uff1a<\/strong><\/span>\u504f\u5fae\u5206\u65b9\u7a0b\u3001\u7b97\u5b50\u5b66\u4e60\u3001\u4eba\u5de5\u667a\u80fd\u3001\u8ba1\u7b97\u7269\u7406<\/strong><\/span><\/section>\n

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          AI for PDE\u548c\u7b97\u5b50\u5b66\u4e60\u6982\u8ff0<\/p>\n<\/li>\n

        2. \n

          Koopman\u7406\u8bba\u5165\u95e8<\/p>\n<\/li>\n

        3. \n
          Koopman\u795e\u7ecf\u7b97\u5b50<\/section>\n<\/li>\n<\/ol>\n

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          \u53c2\u8003\u6587\u732e<\/strong><\/span><\/section>\n
            \n
          1. \n

            Xiong, W., Huang, X., Zhang, Z., Deng, R., Sun, P., & Tian, Y. (2024). Koopman neural operator as a mesh-free solver of non-linear partial differential equations. Journal of Computational Physics<\/em>, 113194.<\/p>\n<\/li>\n

          2. \n

            Xiong, W., Ma, M., Huang, X., Zhang, Z., Sun, P., & Tian, Y. (2023). Koopmanlab: machine learning for solving complex physics equations. APL Machine Learning<\/em>, 1<\/em>(3).<\/p>\n<\/li>\n

          3. \n

            Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2023). Neural operator: Learning maps between function spaces with applications to pdes. Journal of Machine Learning Research<\/em>, 24<\/em>(89), 1-97.<\/p>\n<\/li>\n

          4. \n

            Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895<\/em>.<\/p>\n<\/li>\n

          5. \n

            Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. (2021). Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature machine intelligence<\/em>, 3(3), 218-229.<\/p>\n<\/li>\n

          6. \n

            Brunton, S. L., Budi\u0161i\u0107, M., Kaiser, E., & Kutz, J. N. (2021). Modern Koopman theory for dynamical systems. arXiv preprint arXiv:2102.12086<\/em>.<\/p>\n<\/li>\n<\/ol>\n


            <\/span><\/section>\n
            \u76f8\u5173\u8bfb\u4e66\u4f1a\u5206\u4eab\u63a8\u8350\uff1a<\/span><\/strong><\/span><\/section>\n
            1. \u52a0\u5dde\u7406\u5de5\u5b66\u9662\u674e\u5b97\u5b9c\u5728AI+Science\u8bfb\u4e66\u4f1a\u7684\u5206\u4eab\u300c\u6570\u636e\u9a71\u52a8\u7684\u7269\u7406\u4eff\u771f\u6a21\u62df\uff1a\u795e\u7ecf\u7b97\u5b50\u300d\uff1ahttps:\/\/pattern.swarma.org\/study_group_issue\/478<\/span><\/section>\n

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            \u65f6\u95f4\uff1a<\/strong><\/span>2024\u5e747\u670815\u65e5\uff08\u5468\u4e00\uff09\u665a20:00-22:00<\/span><\/section>\n
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            <\/section>\n

            \u6591\u56fe\u94fe\u63a5\uff1ahttps:\/\/pattern.swarma.org\/study_group\/45?from=wechat<\/span><\/p>\n


            <\/span><\/section>\n
            \u626b\u7801\u53c2\u4e0eAI by Complexity \u8bfb\u4e66\u4f1a<\/a><\/span>\u52a0\u5165\u7fa4\u804a\uff0c\u83b7\u53d6\u7cfb\u5217\u8bfb\u4e66\u4f1a\u56de\u770b\u6743\u9650\uff0c\u52a0\u5165AI by Complexity\u793e\u533a\uff0c\u4e0e\u793e\u533a\u7684\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\u6c9f\u901a\u4ea4\u6d41\uff0c\u5171\u540c\u63a2\u7d22\u590d\u6742\u79d1\u5b66\u4e0eAI\u4ea4\u53c9\u7684\u524d\u6cbf\u9886\u57df\u7684\u53d1\u5c55\u3002<\/span><\/section>\n

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            AI By Complexity\u8bfb\u4e66\u4f1a\u62db\u52df\u4e2d<\/strong>
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            \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
            AI by Complexity \u8bfb\u4e66\u4f1a\u542f\u52a8\uff1a\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf<\/span><\/a>
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