{"id":57025,"date":"2025-02-10T20:03:20","date_gmt":"2025-02-10T12:03:20","guid":{"rendered":"https:\/\/swarma.org\/?p=57025"},"modified":"2025-02-10T20:03:20","modified_gmt":"2025-02-10T12:03:20","slug":"nat-commun-%e9%80%9f%e9%80%92%ef%bc%9a%e5%9f%ba%e4%ba%8e%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e5%99%aa%e9%9f%b3%e8%af%b1%e5%af%bc%e5%8a%a8%e5%8a%9b%e5%ad%a6","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=57025","title":{"rendered":"Nat. Commun. \u901f\u9012\uff1a\u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u566a\u97f3\u8bf1\u5bfc\u52a8\u529b\u5b66"},"content":{"rendered":"
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\u8fd1\u65e5\uff0c\u7535\u5b50\u79d1\u6280\u5927\u5b66\u57fa\u7840\u4e0e\u524d\u6cbf\u7814\u7a76\u9662\u7684\u6c64\u8fce\u6559\u6388\u8bfe\u9898\u7ec4\uff0c\u8054\u5408\u5317\u4eac\u5e08\u8303\u5927\u5b66\u73e0\u6d77\u6821\u533a\u590d\u6742\u7cfb\u7edf\u56fd\u9645\u79d1\u5b66\u4e2d\u5fc3\u7684\u72c4\u589e\u5982\u6559\u6388\u5728Nature Communications<\/em>\u4e0a\u53d1\u8868\u4e86\u9898\u4e3a\u201cLearning noise-induced transitions by multi-scaling reservoir computing\u201d\u7684\u7814\u7a76\u8bba\u6587\u3002\u8bba\u6587\u62a5\u9053\u4e86\u5229\u7528\u591a\u5c3a\u5ea6\u5316\u50a8\u5907\u6c60\u8ba1\u7b97\u5b66\u4e60\u566a\u97f3\u8bf1\u5bfc\u7684\u968f\u673a\u8f6c\u79fb\u7684\u6700\u65b0\u8fdb\u5c55\u3002\u57fa\u7840\u9662\u6c64\u8fce\u6559\u6559\u6388\u4e3a\u901a\u8baf\u4f5c\u8005\uff0c\u56e2\u961f\u5b66\u751f\u6797\u6cfd\u7fa4\u4e3a\u5171\u540c\u7b2c\u4e00\u4f5c\u8005\uff0c\u7535\u5b50\u79d1\u6280\u5927\u5b66\u57fa\u7840\u4e0e\u524d\u6cbf\u7814\u7a76\u9662\u4e3a\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\u5355\u4f4d\u3002<\/span><\/span><\/strong><\/span><\/strong>\u672c\u5468\u56db\u665a\u4e0a19\u70b9\uff0c\u6c64\u8fce\u6559\u6388\u4f1a\u5e26\u6765\u4e3b\u9898\u4e3a\u201c\u673a\u5668\u5b66\u4e60\u5e94\u7528\u4e8e\u968f\u673a\u52a8\u529b\u5b66\u201d\u7684\u5206\u4eab<\/a>\uff0c\u6b22\u8fce\u5927\u5bb6\u9884\u7ea6\u8bfb\u4e66\u4f1a\u62a5\u540d\u3002<\/span><\/span><\/strong><\/span><\/strong><\/p>\n


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\u8bba\u6587\u9898\u76ee\uff1aLearning noise-induced transitions by multi-scaling reservoir computing
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\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/www.nature.com\/articles\/s41467-024-50905-w<\/span><\/p>\n<\/section>\n<\/section>\n<\/blockquote>\n

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\u8fd1\u5e74\u6765\uff0c\u6709\u8bb8\u591a\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u81f4\u529b\u4e8e\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u52a8\u529b\u5b66\u3002\u4e00\u79cd\u65b9\u6cd5\u4f7f\u7528\u7a00\u758f\u8bc6\u522b\u975e\u7ebf\u6027\u52a8\u529b\u5b66<\/span>\uff08SINDy\uff09<\/span>\u6765\u8bc6\u522b\u975e\u7ebf\u6027\u52a8\u529b\u5b66\u3001\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u53bb\u566a\u5e76\u4ece\u6570\u636e\u4e2d\u53c2\u6570\u5316\u566a\u58f0\u6982\u7387\u5206\u5e03\u3002\u7531\u4e8e\u4f18\u5316\u95ee\u9898\u7684\u975e\u51f8\u6027\uff0c\u8be5\u65b9\u6cd5\u53ef\u80fd\u96be\u4ee5\u7a33\u5065\u5730\u5904\u7406\u5927\u578b\u51fd\u6570\u5e93\u7684\u56de\u5f52\u3002\u53e6\u4e00\u7c7b\u65b9\u6cd5\u5219\u91c7\u7528\u7269\u7406\u542f\u53d1\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u6570\u636e\u9a71\u52a8\u7684\u504f\u5fae\u5206\u65b9\u7a0b\u6c42\u89e3\u548c\u53d1\u73b0\uff0c\u6216\u4ece\u6570\u636e\u4e2d\u63d0\u53d6Koopman\u7279\u5f81\u51fd\u6570\u3002\u7136\u800c\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u9700\u8981\u5927\u91cf\u7684\u6570\u636e\u6765\u8bad\u7ec3\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff0c\u5e76\u5bf9\u7f51\u7edc\u8fdb\u884c\u7cbe\u7ec6\u8c03\u6574\u3002<\/span><\/p>\n


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\u5c3d\u7ba1\u4e0a\u8ff0\u65b9\u6cd5\u5177\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u4f46\u5b83\u4eec\u5c1a\u672a\u88ab\u7528\u4e8e\u7814\u7a76\u566a\u58f0\u8bf1\u5bfc\u7684\u8f6c\u53d8\u3002\u4e3a\u4e86\u5b66\u4e60\u566a\u58f0\u8bf1\u5bfc\u7684\u8f6c\u53d8\uff0c\u4f5c\u8005\u9996\u5148\u5229\u7528SINDy [2, 3] \u548c\u5faa\u73af\u795e\u7ecf\u7f51\u7edc<\/span> (RNN)<\/span> [4] \u5bf9\u542b\u6709\u566a\u58f0\u7684\u6570\u636e\u8fdb\u884c\u5904\u7406\u3002\u4f5c\u8005\u53d1\u73b0\uff0c\u5373\u4f7f\u5728\u5177\u6709\u9ad8\u65af\u767d\u566a\u58f0\u7684\u4e00\u7ef4\u53cc\u7a33\u7cfb\u7edf\u4e2d\uff0cSINDy\u548cRNN\u4e5f\u65e0\u6cd5\u51c6\u786e\u9884\u6d4b\u968f\u673a\u8f6c\u79fb\u3002\u4f5c\u8005\u8fd8\u5bf9\u6570\u636e\u5e94\u7528\u4e86\u6ee4\u6ce2\u5668 [5, 6]\uff0c\u83b7\u5f97\u5e73\u6ed1\u7684\u65f6\u95f4\u5e8f\u5217\uff0c\u7136\u540e\u4f7f\u7528SINDy \u5904\u7406\u6ee4\u6ce2\u540e\u7684\u6570\u636e\uff0c\u4f46\u8fd9\u79cd\u65b9\u6cd5\u4ecd\u672a\u80fd\u51c6\u786e\u6355\u6349\u566a\u58f0\u8bf1\u5bfc\u7684\u8f6c\u53d8\u3002\u540c\u6837\uff0cFirst-Order, Reduced, and Controlled Error (FORCE) \u5b66\u4e60\u65b9\u6cd5 [7]\uff0c\u5305\u62ec\u5176\u5404\u79cd\u7248\u672c\u5982full-FORCE\u548c\u5c16\u5cf0\u795e\u7ecf\u5143\u6a21\u578b [8]\uff0c\u5728\u5b9e\u9a8c\u6570\u636e\u4e2d\u4e5f\u672a\u80fd\u5b8c\u5168\u6355\u6349\u968f\u673a\u8f6c\u79fb\uff0c\u5e76\u4e14\u9700\u8981\u76f8\u5bf9\u8f83\u9ad8\u7684\u8ba1\u7b97\u6210\u672c\u3002\u8fd9\u4e9b\u5c1d\u8bd5\u8868\u660e\uff0c\u8fd9\u4e9b\u4f20\u7edf\u65b9\u6cd5\u4e3b\u8981\u8bbe\u8ba1\u7528\u4e8e\u5bf9\u566a\u58f0\u6570\u636e\u8fdb\u884c\u53bb\u566a\uff0c\u4ee5\u5b66\u4e60\u786e\u5b9a\u6027\u52a8\u529b\u5b66\uff0c\u800c\u975e\u6355\u6349\u566a\u58f0\u8bf1\u5bfc\u7684\u73b0\u8c61\u3002\u56e0\u6b64\uff0c\u9700\u8981\u5f00\u53d1\u4e00\u79cd\u65b0\u65b9\u6cd5\u6765\u9884\u6d4b\u968f\u673a\u8f6c\u79fb\u3002<\/span><\/p>\n


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\u4f5c\u8005\u6ce8\u610f\u5230\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u67b6\u6784\u2014\u2014\u50a8\u5907\u6c60\u8ba1\u7b97 (RC) [9-11]\uff0c\u53ef\u80fd\u9002\u5408\u8fd9\u4e00\u4efb\u52a1\u3002\u50a8\u5907\u6c60\u8ba1\u7b97\u7684\u8bad\u7ec3\u53ea\u9700\u8981\u7ebf\u6027\u56de\u5f52\uff0c\u8fd9\u6bd4\u9700\u8981\u53cd\u5411\u4f20\u64ad\u7684\u795e\u7ecf\u7f51\u7edc\u5728\u8ba1\u7b97\u4e0a\u66f4\u4e3a\u5feb\u901f\u3002\u7814\u7a76\u53d1\u73b0\uff0c\u50a8\u5907\u6c60\u8ba1\u7b97\u5728\u5b66\u4e60\u52a8\u529b\u7cfb\u7edf\u65b9\u9762\u6548\u679c\u663e\u8457\uff0c\u5305\u62ec\u6df7\u6c8c\u7cfb\u7edf\u3002\u6b64\u524d\u66fe\u6709\u4e00\u4e2a\u5de5\u4f5c\u5c1d\u8bd5\u4f7f\u7528RC\u6765\u5904\u7406\u566a\u58f0\u8bf1\u5bfc\u7684\u8f6c\u53d8 [12]\uff0c\u4f46\u4f9d\u8d56\u4e8e\u4e8b\u5148\u77e5\u9053\u786e\u5b9a\u6027\u52a8\u529b\u5b66\u65b9\u7a0b\u7684\u5047\u8bbe\uff0c\u8fd9\u5728\u5b9e\u9645\u95ee\u9898\u4e2d\u8fc7\u5f3a\u3002\u5b9e\u9645\u4e0a\uff0c\u786e\u5b9a\u6027\u52a8\u529b\u5b66\u7684\u5148\u9a8c\u77e5\u8bc6\u5f80\u5f80\u7f3a\u4e4f\uff0c\u6709\u65f6\u751a\u81f3\u65e0\u6cd5\u901a\u8fc7\u65b9\u7a0b\u76f4\u63a5\u63cf\u8ff0\u3002\u90a3\u4e48\u80fd\u5426\u4ec5\u57fa\u4e8e\u6570\u636e\uff0c\u5728\u6ca1\u6709\u4efb\u4f55\u786e\u5b9a\u6027\u65b9\u7a0b\u7684\u5148\u9a8c\u77e5\u8bc6\u7684\u60c5\u51b5\u4e0b\u9884\u6d4b\u566a\u58f0\u8bf1\u5bfc\u7684\u8f6c\u53d8\u5462\uff1f<\/span><\/p>\n


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\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u4f5c\u8005\u5f00\u53d1\u4e86\u4e00\u79cd\u591a\u5c3a\u5ea6\u50a8\u5907\u6c60\u8ba1\u7b97\u6846\u67b6\uff0c\u7528\u4e8e\u5728\u65e0\u6a21\u578b\u7684\u60c5\u51b5\u4e0b\u5b66\u4e60\u566a\u58f0\u8bf1\u5bfc\u7684\u8f6c\u53d8\u3002\u8be5\u65b9\u6cd5\u7684\u7075\u611f\u6765\u81ea\u4e8e\u7814\u7a76\u53d1\u73b0\u50a8\u5907\u6c60\u4e2d\u7684\u8d85\u53c2\u6570  \u51b3\u5b9a\u4e86\u50a8\u5907\u6c60\u52a8\u529b\u5b66\u7684\u65f6\u95f4\u5c3a\u5ea6 [13]\u3002\u9274\u4e8e\u591a\u5c3a\u5ea6\u65f6\u95f4\u5e8f\u5217\uff0c\u53ef\u4ee5\u8c03\u8282\u8d85\u53c2\u6570  \u4ee5\u5339\u914d\u6162\u65f6\u95f4\u5c3a\u5ea6\u7684\u52a8\u529b\u5b66\u3002\u5f53\u50a8\u5907\u6c60\u901a\u8fc7\u62df\u5408\u8f93\u51fa\u5c42\u77e9\u9635\u6355\u6349\u5230\u6162\u65f6\u95f4\u5c3a\u5ea6\u7684\u52a8\u529b\u5b66\u540e\uff0c\u53ef\u4ee5\u5c06\u5feb\u65f6\u95f4\u5c3a\u5ea6\u7684\u5e8f\u5217\u5206\u79bb\u4e3a\u4e00\u4e2a\u566a\u58f0\u5206\u5e03\u3002\u5728\u9884\u6d4b\u9636\u6bb5\uff0c\u4f5c\u8005\u5229\u7528\u8bad\u7ec3\u597d\u7684\u50a8\u5907\u6c60\u8ba1\u7b97\u673a\u6765\u6a21\u62df\u6162\u65f6\u95f4\u5c3a\u5ea6\u7684\u52a8\u529b\u5b66\uff0c\u7136\u540e\u5c06\u4ece\u5206\u79bb\u7684\u566a\u58f0\u5206\u5e03\u4e2d\u91c7\u6837\u7684\u566a\u58f0<\/span>\uff08\u5bf9\u4e8e\u767d\u566a\u58f0\uff09<\/span>\u6216\u4ece\u7b2c\u4e8c\u4e2a\u50a8\u5907\u6c60\u4e2d\u5b66\u4e60\u7684\u566a\u58f0<\/span>\uff08\u5bf9\u4e8e\u5f69\u8272\u566a\u58f0\uff09<\/span>\u52a0\u56de\u3002\u8fd9\u4e2a\u65b9\u6cd5\u4e0e\u4e4b\u524d\u5c06\u566a\u58f0\u4ec5\u89c6\u4e3a\u5e72\u6270\u56e0\u7d20\u7684\u65b9\u6cd5\u6709\u6240\u4e0d\u540c\u3002<\/span><\/p>\n


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\u4e3a\u4e86\u8bc1\u660e\u5f53\u524d\u65b9\u6cd5\u7684\u6709\u6548\u6027\uff0c\u4f5c\u8005\u5c06\u5176\u5e94\u7528\u4e8e\u4e24\u7c7b\u573a\u666f\u3002\u7b2c\u4e00\u7c7b\u573a\u666f\u7684\u6570\u636e\u6765\u81ea\u4e8e\u968f\u673a\u5fae\u5206\u65b9\u7a0b<\/span> (SDE)<\/span>\uff0c\u7528\u4e8e\u6d4b\u8bd5\u8be5\u65b9\u6cd5\uff1b\u7b2c\u4e8c\u7c7b\u573a\u666f\u7684\u6570\u636e\u5219\u662f\u5b9e\u9a8c\u6570\u636e [6]\u3002\u5bf9\u4e8e\u7b2c\u4e00\u7c7b\u573a\u666f\u4e2d\u7684\u767d\u566a\u58f0\uff0c\u5305\u542b\u4e00\u7ef4<\/span> (1D) <\/span>\u53cc\u7a33\u68af\u5ea6\u7cfb\u7edf\u3001\u4e8c\u7ef4<\/span> (2D) <\/span>\u53cc\u7a33\u68af\u5ea6\u548c\u975e\u68af\u5ea6\u7cfb\u7edf\u3001\u5177\u6709\u503e\u659c\u52bf\u7684\u4e00\u7ef4\u548c\u4e8c\u7ef4\u68af\u5ea6\u7cfb\u7edf\u3001\u4e8c\u7ef4\u503e\u659c\u975e\u68af\u5ea6\u7cfb\u7edf\u4ee5\u53ca\u4e8c\u7ef4\u4e09\u7a33\u7cfb\u7edf\u3002\u8be5\u65b9\u6cd5\u80fd\u591f\u6355\u6349\u8f6c\u53d8\u65f6\u95f4\u7684\u7edf\u8ba1\u7279\u5f81\u548c\u8f6c\u53d8\u6b21\u6570\u3002\u5bf9\u4e8e\u7b2c\u4e00\u7c7b\u573a\u666f\u4e2d\u7684\u5f69\u8272\u566a\u58f0\uff0c\u4f5c\u8005\u7814\u7a76\u4e86\u5177\u6709Lorenz\u566a\u58f0<\/span>\uff08Lorenz-63\u6a21\u578b\u548cLorenz-96\u6a21\u578b [12]\uff09<\/span>\u7684\u53cc\u7a33\u68af\u5ea6\u7cfb\u7edf\uff0c\u80fd\u591f\u5728\u4e0d\u5047\u8bbe\u77e5\u9053\u52a8\u529b\u5b66\u786e\u5b9a\u6027\u90e8\u5206\u7684\u524d\u63d0\u4e0b\u51c6\u786e\u9884\u6d4b\u7279\u5b9a\u7684\u8f6c\u53d8\u65f6\u95f4\uff0c\u8fd9\u533a\u522b\u4e8e [12] \u4e2d\u7684\u8981\u6c42\u3002\u5bf9\u4e8e\u7b2c\u4e8c\u7c7b\u573a\u666f\uff0c\u4f5c\u8005\u5c06\u8be5\u65b9\u6cd5\u5e94\u7528\u4e8e\u86cb\u767d\u8d28\u6298\u53e0\u6570\u636e\uff0c\u5e76\u63a2\u7d22\u4e86\u51c6\u786e\u8bad\u7ec3\u6240\u9700\u7684\u6700\u5c11\u6570\u636e\u91cf\uff0c\u8fd9\u6709\u52a9\u4e8e\u51cf\u5c11\u5b9e\u9a8c\u4e2d\u5bf9\u5e7f\u6cdb\u6d4b\u91cf\u7684\u9700\u6c42\u3002\u8fd9\u91cc\u5c55\u793a\u4e86\u8be5\u65b9\u6cd5\u5728\u90e8\u5206\u4f8b\u5b50\u4e2d\u7684\u7ed3\u679c\uff0c\u5305\u62ec\u542b\u767d\u566a\u97f3\u7684\u4e00\u7ef4\u53cc\u7a33\u6001\u7cfb\u7edf<\/span>\uff08\u56fe2\uff09<\/span>\u548c\u5b9e\u9a8c\u4e0a\u7684\u86cb\u767d\u8d28\u7aef\u5230\u7aef\u957f\u5ea6\u6570\u636e<\/span>\uff08\u56fe3\uff09<\/span>\u7684\u7ed3\u679c\u3002<\/span><\/p>\n


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\u603b\u7684\u6765\u8bf4\uff0c\u4f5c\u8005\u63d0\u4f9b\u4e86\u4e00\u4e2a\u57fa\u4e8e\u968f\u673a\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u901a\u7528\u6846\u67b6\uff0c\u7528\u4e8e\u5b66\u4e60\u566a\u58f0\u8bf1\u5bfc\u7684\u968f\u673a\u8f6c\u6362\u3002\u4f5c\u8005\u5c06\u8fd9\u79cd\u65b9\u6cd5\u5e94\u7528\u4e8e\u968f\u673a\u5fae\u5206\u65b9\u7a0b\u548c\u5b9e\u9a8c\u6570\u636e\u7684\u4f8b\u5b50\u4e2d\uff0c\u8be5\u65b9\u6cd5\u80fd\u591f\u4ece\u5c0f\u89c4\u6a21\u7684\u8bad\u7ec3\u96c6\u4e2d\u51c6\u786e\u5b66\u4e60\u8f6c\u6362\u7edf\u8ba1\u91cf\u3002\u672c\u9879\u7814\u7a76\u80fd\u591f\u63a8\u52a8\u5bf9\u566a\u58f0\u8bf1\u5bfc\u73b0\u8c61\u7684\u7cfb\u7edf\u6027\u7814\u7a76\uff0c\u8d85\u51fa\u73b0\u6709\u5728\u63d0\u53d6\u786e\u5b9a\u6027\u52a8\u529b\u5b66\u65f6\u5355\u7eaf\u964d\u4f4e\u566a\u58f0\u5f71\u54cd\u7684\u8303\u7574\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u62d3\u5c55SINDy\u7b97\u6cd5\u548cFORCE\u5b66\u4e60\u65b9\u6cd5\u7684\u6846\u67b6\uff0c\u6df1\u5316\u5bf9\u566a\u58f0\u8bf1\u5bfc\u73b0\u8c61\u7684\u8ba4\u8bc6\u548c\u5e94\u7528\u3002<\/span><\/p>\n


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[1] R. Tapia-Rojo, M. Mora, S. Board, J. Walker, R. Boujemaa-Paterski, O. Medalia, and S. Garcia-Manyes, Enhanced statistical sampling reveals microscopic complexity in the talin mechanosensor folding energy landscape, Nat. Phys. 19, 52 (2023). <\/span><\/p>\n

[2] S. L. Brunton, J. L. Proctor, and J. N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, Proc. Natl. Acad. Sci. 113, 3932 (2016). <\/span><\/p>\n

[3] K. Kaheman, S. L. Brunton, and J. N. Kutz, Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data, Mach. Learn.: Sci. Technol. 3, 015031 (2022). <\/span><\/p>\n

[4] S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9, 1735 (1997). <\/span><\/p>\n

[5] R. R. Labbe, Filterpy documentation (2018). <\/span><\/p>\n

[6] P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., Scipy 1.0: fundamental algorithms for scientific computing in python, Nat. Methods 17, 261 (2020). <\/span><\/p>\n

[7] D. Sussillo and L. F. Abbott, Generating coherent patterns of activity from chaotic neural networks, Neuron 63, 544 (2009). <\/span><\/p>\n

[8] L. B. Liu, A. Losonczy, and Z. Liao, tension: A python package for force learning, PLOS Comput. Biol. 18, e1010722 (2022). <\/span><\/p>\n

[9] H. Jaeger, The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note, Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148, 13 (2001). <\/span><\/p>\n

[10] J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott, Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach, Phys. Rev. Lett. 120, 024102 (2018). <\/span><\/p>\n

[11] T. L. Carroll, Using reservoir computers to distinguish chaotic signals, Phys. Rev. E 98, 052209 (2018). <\/span><\/p>\n

[12] S. H. Lim, L. Theo Giorgini, W. Moon, and J. S. Wettlaufer, Predicting critical transitions in multiscale dynamical systems using reservoir computing, Chaos 30, 123126 (2020). <\/span><\/p>\n

[13] G. Tanaka, T. Matsumori, H. Yoshida, and K. Aihara, Reservoir computing with diverse timescales for prediction of multiscale dynamics, Phys. Rev. Res. 4, L032014 (2022). <\/span><\/p>\n


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https:\/\/www.nature.com\/articles\/s41467-024-50905-w<\/span><\/p>\n<\/section>\n

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\u6c64\u8fce\u8001\u5e08\u7684\u8bfb\u4e66\u4f1a\u5206\u4eab<\/span><\/strong><\/span><\/p>\n

\u6b22\u8fce\u626b\u7801\u62a5\u540d<\/span><\/strong><\/span><\/section>\n

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\u975e\u5e73\u8861\u7edf\u8ba1\u7269\u7406\u8bfb\u4e66\u4f1a\u542f\u52a8\uff01<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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2024\u5e74\u8bfa\u8d1d\u5c14\u7269\u7406\u5b66\u5956\u6388\u4e88\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\uff0c\u8fd9\u662f\u4e00\u573a\u7edf\u8ba1\u7269\u7406\u5f15\u53d1\u7684\u673a\u5668\u5b66\u4e60\u9769\u547d\u3002\u7edf\u8ba1\u7269\u7406\u5b66\u4e0d\u4ec5\u80fd\u89e3\u91ca\u70ed\u5b66\u73b0\u8c61\uff0c\u8fd8\u80fd\u5e2e\u52a9\u6211\u4eec\u7406\u89e3\u4ece\u5fae\u89c2\u7c92\u5b50\u5230\u5b8f\u89c2\u5b87\u5b99\u7684\u5404\u4e2a\u5c42\u7ea7\u5982\u4f55\u8054\u7cfb\u8d77\u6765\uff0c\u590d\u6742\u73b0\u8c61\u5982\u4f55\u6d8c\u73b0\u3002\u5b83\u901a\u8fc7\u7814\u7a76\u5927\u91cf\u7c92\u5b50\u7684\u96c6\u4f53\u884c\u4e3a\uff0c\u6210\u529f\u5730\u5c06\u5fae\u89c2\u4e16\u754c\u7684\u968f\u673a\u6027\u4e0e\u5b8f\u89c2\u4e16\u754c\u7684\u786e\u5b9a\u6027\u8054\u7cfb\u8d77\u6765\uff0c\u4e3a\u6211\u4eec\u7406\u89e3\u81ea\u7136\u754c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u4e5f\u4e3a\u673a\u5668\u5b66\u4e60\u548c\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u53d1\u5c55\u63d0\u4f9b\u4e86\u91cd\u8981\u63a8\u52a8\u529b\u3002<\/span><\/p>\n


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\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/strong><\/section>\n
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1. <\/strong>Nat.Commun.\u524d\u6cbf\uff1a\u53d8\u5206\u81ea\u56de\u5f52\u7f51\u7edc\u6c42\u89e3\u975e\u5e73\u8861\u6001\u7edf\u8ba1\u529b\u5b66\u548c\u52a8\u529b\u5b66\u76f8\u53d8<\/a><\/strong><\/span><\/strong><\/section>\n
2. <\/strong>Nat. Mach. Intell. \u901f\u9012\uff1a\u901a\u8fc7\u795e\u7ecf\u7f51\u7edc\u6c42\u89e3\u968f\u673a\u5316\u5b66\u53cd\u5e94\u7f51\u7edc<\/a><\/strong><\/span><\/strong><\/section>\n
3. <\/strong>\u7269\u7406\u5b66\u9047\u89c1\u673a\u5668\u5b66\u4e60\uff1a\u6df1\u5ea6\u5b66\u4e60\u7684\u4e0b\u4e00\u4e2a\u7a81\u7834\u53ef\u80fd\u6765\u81ea\u7edf\u8ba1\u7269\u7406\u5b66<\/a><\/strong><\/span><\/strong><\/section>\n
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4. <\/strong><\/span>\u5f20\u6c5f\uff1a\u7b2c\u4e09\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u57fa\u7840\u2014\u2014\u4ece\u53ef\u5fae\u5206\u7f16\u7a0b\u5230\u56e0\u679c\u63a8\u7406 | \u96c6\u667a\u5b66\u56ed\u5168\u65b0\u8bfe\u7a0b<\/a><\/strong><\/strong><\/span><\/section>\n
<\/strong>5. <\/strong>\u89e3\u9501\u96c6\u667a\u5168\u7ad9\u5185\u5bb9\uff0c\u5f00\u542f\u65b0\u5e74\u5b66\u4e60\u8ba1\u5212<\/a><\/strong><\/section>\n

6. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/p>\n

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\u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u62a5\u540d\u8bfb\u4e66\u4f1a<\/span><\/section>\n<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

\u6982\u8981 \u8fd1\u65e5\uff0c\u7535\u5b50\u79d1\u6280\u5927\u5b66\u57fa\u7840\u4e0e\u524d\u6cbf\u7814\u7a76\u9662\u7684\u6c64\u8fce\u6559\u6388\u8bfe\u9898\u7ec4\uff0c\u8054\u5408\u5317\u4eac\u5e08\u8303\u5927\u5b66\u73e0\u6d77\u6821\u533a\u590d\u6742\u7cfb\u7edf\u56fd\u9645\u79d1\u5b66\u4e2d\u5fc3\u7684\u72c4\u589e\u5982\u6559\u6388\u5728Nature Communications\u4e0a\u53d1\u8868\u4e86\u9898\u4e3a\u201cLearning noise-induced transitions by multi-scaling reservoir co…<\/p>\n","protected":false},"author":1,"featured_media":57012,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/57025"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=57025"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/57025\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/57012"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=57025"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=57025"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=57025"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=57025"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}