{"id":52459,"date":"2024-09-02T20:58:36","date_gmt":"2024-09-02T12:58:36","guid":{"rendered":"https:\/\/swarma.org\/?p=52459"},"modified":"2024-09-02T20:58:36","modified_gmt":"2024-09-02T12:58:36","slug":"%e5%9f%ba%e4%ba%8e%e5%87%a0%e4%bd%95%e5%9b%be%e5%ad%a6%e4%b9%a0%e7%9a%84%e7%89%a9%e7%90%86%e7%b3%bb%e7%bb%9f%e5%bb%ba%e6%a8%a1%ef%bc%9a%e4%bb%8e%e5%8a%a8%e6%80%81%e7%b3%bb%e7%bb%9f%e5%88%b0%e5%a4%8d","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=52459","title":{"rendered":"\u57fa\u4e8e\u51e0\u4f55\u56fe\u5b66\u4e60\u7684\u7269\u7406\u7cfb\u7edf\u5efa\u6a21\uff1a\u4ece\u52a8\u6001\u7cfb\u7edf\u5230\u590d\u6742\u7cfb\u7edf"},"content":{"rendered":"
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    \u8bb2\u5230\u8fd9\u91cc\uff0c\u6211\u4eec\u5c31\u8981\u5f15\u5165\u4e00\u4e2a\u6982\u5ff5\uff0c\u53eb\u504f\u7f6e\u5047\u8bbe\uff08Inductive Bias\uff09\u3002\u8fd9\u4e2a\u672f\u8bed\u7528\u6765\u5f62\u5bb9\u4e00\u4e9b\u7cfb\u7edf\u4e2d\u6700\u57fa\u672c\u7684\u6027\u8d28\uff0c\u6700\u57fa\u672c\u7684\u5148\u9a8c\u77e5\u8bc6\u3002\u5728\u51e0\u4f55\u56fe\u4e2d\uff0c\u8fd9\u79cd\u5047\u8bbe\u88ab\u79f0\u4e3a\u201c\u7b49\u53d8\u6027\u201d\u3002<\/strong><\/span><\/section>\n

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    <\/span><\/section>\n
    \u6211\u4eec\u5904\u7406\u51e0\u4f55\u56fe\uff0c\u4e00\u822c\u91c7\u7528\u51e0\u4f55\u56fe\u795e\u7ecf\u7f51\u7edc\u3002\u5b83\u4e0e\u4f20\u7edf\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u6700\u6838\u5fc3\u533a\u522b\u5c31\u662f\u4fdd\u6301\u4e86\u7b49\u53d8\u6027\u3002\u6211\u4eec\u53ef\u4ee5\u4ece\u4e0b\u9762\u8fd9\u5f20\u56fe\u6240\u5c55\u793a\u7684\u6d88\u606f\u4f20\u9012\u65b9\u5f0f\u5bf9\u6bd4\u5b83\u4e0e\u4f20\u7edf\u7f51\u7edc\u7684\u533a\u522b\u3002\u4f20\u7edf\u7f51\u7edc\u4ec5\u6709\u9ed1\u8272<\/span>\uff081\u30013\u884c\uff09<\/span>\u6240\u793a\u7684\u4f20\u9012\u65b9\u5f0f\uff1b\u5728\u84dd\u8272\u90e8\u5206\u589e\u52a0\u4e86\u51e0\u4f55\u4fe1\u606f\u7684\u751f\u6210\u548c\u4f20\u9012\u3002\u53ef\u4ee5\u770b\u5230\uff0c\u901a\u8fc7\u5e76\u884c\u8fdb\u884c\u6d88\u606f\u751f\u6210\u4e0e\u4f20\u9012\uff0c\u51e0\u4f55\u56fe\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u5c06\u51e0\u4f55\u4fe1\u606f\u5145\u5206\u878d\u5165\uff0c\u8fdb\u800c\u4fdd\u6301\u7b49\u53d8\u6027\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u8c08\u53ca\u7b49\u53d8\u795e\u7ecf\u7f51\u7edc\uff0c\u5c31\u4e0d\u5f97\u4e0d\u63d0\u5230\u8fd9\u7bc7EGNN[1]\u5de5\u4f5c\uff0c\u5b83\u7528\u4e00\u79cd\u5f88\u7b80\u5355\u7684\u6807\u91cf\u5316\u64cd\u4f5c<\/strong>\u2014\u2014\u9996\u5148\u91c7\u7528\u8ba1\u7b97\u6b27\u6c0f\u8ddd\u79bb\u7684\u5f62\u5f0f\u5f97\u5230\u4e00\u4e2a\u6807\u91cf\u6570\u503c\uff0c\u8fdb\u884c\u5e38\u89c4\u6d88\u606f\u4f20\u9012\uff0c\u5f53\u66f4\u65b0\u51e0\u4f55\u4fe1\u606f\u65f6<\/span>\uff08\u8fd9\u91cc\u5177\u4f53\u5316\u4e3a\u5750\u6807x\uff09<\/span>\u7528xi<\/sub>– <\/sub>xj<\/sub>\u7684\u5f62\u5f0f\u4ea7\u751f\u4e00\u4e2a\u5e26\u65b9\u5411\u7684\u5411\u91cf\uff0c\u8fdb\u884c\u52a0\u6743\u5e73\u5747\u6c42\u548c\u66f4\u65b0\u5750\u6807\u3002\u8fd9\u6837\u7684\u65b9\u5f0f\u867d\u7136\u5f88\u7b80\u5355\uff0c\u4f46\u662f\u5374\u5f88\u6709\u7528\uff0c\u540e\u9762\u57fa\u4e8eEGNN\u7684\u5de5\u4f5c\u4e5f\u90fd\u662f\u4ece\u8fd9\u4e2a\u57fa\u7840\u4e0a\u8fdb\u884c\u6539\u8fdb\u3002\u56e0\u6b64\uff0c\u8fd9\u7bc7\u6587\u7ae0\u662f\u5f53\u4e4b\u65e0\u6127\u7684\u7ecf\u5178\u548c\u9f3b\u7956\uff0c\u503c\u5f97\u6211\u4eec\u4ed4\u7ec6\u7814\u8bfb\u3002\u6709\u5173\u66f4\u591a\u7684\u7b49\u53d8\u7f51\u7edc\u6784\u9020\u7684\u65b9\u5f0f\u548c\u76f8\u5173\u7684\u5e94\u7528\u4e5f\u53ef\u4ee5\u53c2\u9605\u6211\u4eec\u6700\u8fd1\u53d1\u5e03\u7684\u7efc\u8ff0[6]\u3002<\/span><\/section>\n

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    \n
    \n
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    <\/svg><\/section>\n<\/section>\n<\/section>\n
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    3. \u9488\u5bf9\u52a8\u6001\u7cfb\u7edf\u7684\u5efa\u6a21<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

    \n

    <\/section>\n

    <\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


    <\/p>\n

    \u6211\u4eec\u8fd9\u91cc\u6240\u8c08\u5230\u7684\u52a8\u6001\u7cfb\u7edf\u5efa\u6a21\uff0c\u5b9e\u9645\u4e0a\u53ef\u4ee5\u5f52\u7eb3\u4e3a\u4e00\u4e2a\u81ea\u56de\u5f52\u95ee\u9898\uff0c\u8f93\u5165t\u65f6\u523b\u7684\u4e00\u79cd\u7c92\u5b50\u72b6\u6001\u56fe\uff0c\u4ea7\u751f\u4e0b\u4e00\u65f6\u523b\u7684\u7c92\u5b50\u56fe\u3002<\/span><\/section>\n

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    <\/span><\/section>\n
    \u7136\u800c\uff0c\u8fd9\u79cd\u9884\u6d4b\u662f\u6709\u4e9b\u8bb8\u95ee\u9898\u7684\uff0c\u9700\u8981\u989d\u5916\u77e5\u8bc6\u7684\u5d4c\u5165\u3002\u5728\u63a5\u4e0b\u6765\u7684\u8bb2\u89e3\u4e2d\uff0c\u6211\u4eec\u5c06\u9610\u8ff0\u5efa\u6a21\u52a8\u6001\u7cfb\u7edf\u65f6\u7684\u4e00\u4e9b\u95ee\u9898\uff0c\u9488\u5bf9\u8fd9\u4e9b\u95ee\u9898\uff0c\u6211\u4eec\u878d\u5165\u4e86\u54ea\u4e9b\u5148\u9a8c\u504f\u7f6e\u5047\u8bbe\u4ece\u800c\u6539\u8fdb\u9884\u6d4b\u6548\u679c\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    \u4e0b\u9762\u4e3b\u8981\u4ecb\u7ecd\u4e09\u4e2a\u5de5\u4f5c\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n


    <\/p>\n

    3.1 SEGNO: Second-order Equivariant Graph Neural Ordinary Differential Equation[2]<\/span><\/strong><\/span>
    <\/section>\n

    <\/section>\n
    \u5728\u5efa\u6a21\u52a8\u6001\u7cfb\u7edf\u65f6\uff0c\u7269\u7406\u5b66\u5bb6\u80fd\u591f\u8fd0\u7528\u7ecf\u5178\u7269\u7406\u89c4\u5f8b<\/span>\uff08\u5982\u725b\u987f\u7b2c\u4e8c\u5b9a\u5f8b\uff09<\/span>\u5bf9\u6bcf\u4e2a\u7c92\u5b50\u7684\u8fd0\u52a8\u8fdb\u884c\u6a21\u62df\u3002\u7136\u800c\uff0c\u968f\u7740\u7c92\u5b50\u6570\u7684\u589e\u52a0\uff0c\u8ba1\u7b97\u590d\u6742\u6027\u6025\u5267\u4e0a\u5347\uff0c\u8fd9\u5bf9\u8ba1\u7b97\u8d44\u6e90\u7684\u8981\u6c42\u66f4\u4e3a\u82db\u523b\u3002\u6b64\u5916\uff0c\u968f\u7740\u65f6\u95f4\u548c\u8fed\u4ee3\u6b21\u6570\u7684\u589e\u52a0\uff0c\u6bcf\u4e00\u6b65\u7684\u8bef\u5dee\u4f1a\u9010\u6e10\u7d2f\u79ef\uff0c\u76f4\u81f3\u7cfb\u7edf\u5d29\u6e83\u3002\u8fd9\u4e24\u70b9\u56f0\u96be\u4f7f\u5f97\u957f\u65f6\u7a0b\u9884\u6d4b\u4efb\u52a1\u96be\u5ea6\u9aa4\u7136\u4e0a\u5347\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u7ecf\u8fc7\u6587\u732e\u8c03\u7814\u4e0e\u7ec6\u81f4\u5206\u6790\uff0c\u6211\u4eec\u53d1\u73b0\u6709\u4e24\u70b9\u5148\u9a8c\u77e5\u8bc6\u4e4b\u524d\u6ca1\u6709\u6d89\u53ca\uff1a<\/span><\/section>\n
    1\uff09\u8bb8\u591a\u6a21\u578b\u4ec5\u5173\u6ce8\u4ece\u524d\u4e00\u65f6\u523b\u7684\u4f4d\u7f6e\u9884\u6d4b\u540e\u4e00\u65f6\u523b\u7684\u4f4d\u7f6e<\/span>\uff08\u4ec5\u6709\u4e00\u9636\u4fe1\u606f\uff09<\/span>\u3002\u7136\u800c\u5728\u7269\u7406\u5b66\u9886\u57df\uff0c\u903b\u8f91\u5e94\u8be5\u662f\uff1a\u5206\u6790\u53d7\u529b-\u5f97\u5230\u52a0\u901f\u5ea6-\u6c42\u89e3\u901f\u5ea6-\u6c42\u89e3\u4f4d\u79fb\uff0c\u5e94\u8be5\u589e\u52a0\u5bf9\u52a0\u901f\u5ea6\u7684\u9884\u6d4b\u548c\u8003\u91cf<\/span>\uff08\u4e8c\u9636\u4fe1\u606f\uff09<\/span>\uff1b<\/span><\/section>\n
    2\uff09\u73b0\u6709\u6570\u636e\u6781\u5176\u7a00\u758f\uff0c\u4ec5\u6709\u8fd9\u4e00\u65f6\u523b\u548c\u4e0b\u4e00\u65f6\u523b\u7684\u79bb\u6563\u5316\u6570\u636e\uff0c\u81f3\u4e8e\u4e2d\u95f4\u7ecf\u5386\u4e86\u4ec0\u4e48\u65e0\u6cd5\u77e5\u6653\u3002<\/span><\/section>\n
    \u9488\u5bf9\u8fd9\u4e24\u70b9\u5c40\u9650\uff0c\u6211\u4eec\u8bbe\u8ba1\u4e86SEGNO\u6846\u67b6\uff0c\u5c06\u4e24\u79cd\u5148\u9a8c\u5047\u8bbe\u878d\u5165\u6a21\u578b\u4e2d\u3002<\/span><\/section>\n

    <\/section>\n
    <\/section>\n

    <\/section>\n
    \u5177\u4f53\u800c\u8a00\uff0c\u6a21\u578b\u9996\u5148\u91c7\u7528\u4e00\u4e2aMLP\u7f51\u7edc\u5bf9\u52a0\u901f\u5ea6\u8fdb\u884c\u5efa\u6a21\uff0c\u7136\u540e\u5c06\u52a0\u901f\u5ea6\u8fd9\u4e2a\u53c2\u91cf\u878d\u5165\u51e0\u4f55\u56fe\u795e\u7ecf\u7f51\u7edc\u4e2d\u8fdb\u884c\u6d88\u606f\u4f20\u9012\uff0c\u968f\u540e\u91c7\u7528\u5e38\u5fae\u5206\u65b9\u7a0b<\/span>\uff08ODE\uff09<\/span>\u5bf9\u901f\u5ea6\u3001\u4f4d\u79fb\u7b49\u53c2\u91cf\u8fdb\u884c\u6c42\u89e3\uff1b\u6b64\u5916\uff0c\u901a\u8fc7\u6b27\u62c9\u65b9\u7a0b\u7684\u65b9\u5f0f\uff0c\u6a21\u578b\u80fd\u591f\u5f88\u597d\u5730\u6c42\u51fat0<\/sub>-t1<\/sub>\u65f6\u95f4\u6bb5\u5185\u4ee5\u0394t \u4e3a\u95f4\u9694\u7684\u7c92\u5b50\u4f4d\u7f6e\u548c\u901f\u5ea6\uff0c\u4ece\u800c\u80fd\u591f\u5728\u79bb\u6563\u7684\u8fc7\u7a0b\u4e2d\u63d2\u503c\u672a\u89c2\u6d4b\u91cf\u3002<\/span><\/section>\n

    <\/section>\n
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    <\/section>\n

    <\/section>\n
    \u5728\u8ba1\u7b97\u635f\u5931\u65f6\uff0c\u6a21\u578b\u4ec5\u8ba1\u7b97t1<\/sub>\u65f6\u523b\u7531\u6a21\u578b\u9884\u6d4b\u51fa\u7684\u7c92\u5b50\u4f4d\u7f6e\u548c\u771f\u5b9e\u4f4d\u7f6e\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002<\/span><\/section>\n

    <\/section>\n
    <\/section>\n

    <\/section>\n
    \u5728\u7406\u8bba\u8bc1\u660e\u4e0a\uff0cSEGNO\u80fd\u591f\u8bc1\u5b9e\u6a21\u578b\u5177\u6709\u7b49\u53d8\u6027\uff0c\u89e3\u552f\u4e00\u6027\uff1b\u4e14\u901a\u8fc7\u4e0b\u56fe\u5b9e\u9a8c\u53ef\u770b\u51fa\u4e0e\u4f20\u7edf\u7684EGNN\u76f8\u6bd4\uff0cSEGNO\u5728\u53ef\u4ee5\u4fdd\u8bc1\u5728t0-t1\u8fc7\u7a0b\u4e2d\u7684\u8def\u5f84\u552f\u4e00\u6027\uff0c\u540c\u65f6\u8fd9\u4e00\u8def\u5f84\u4e5f\u53ef\u4ee5\u51c6\u786e\u9884\u6d4bt0.5<\/sub>\u8fd9\u4e2a\u4e2d\u95f4\u91cf\u3002<\/span><\/section>\n

    <\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u4ece\u5177\u4f53\u5b9e\u9a8c\u6765\u770b\uff0cSEGNO\u4e0d\u4ec5\u80fd\u5728\u5b9a\u91cf\u5b9e\u9a8c<\/span>\uff08\u5982\u65b9\u5411\u9884\u6d4b\u5b9e\u9a8c\uff09<\/span>\u4e2d\u53d6\u5f97\u8f83\u9ad8\u6307\u6807\uff0c\u5747\u65b9\u8bef\u5dee<\/span>\uff08MSE\uff09<\/span>\u8f83\u5c0f\uff1b\u540c\u65f6\u4e5f\u5341\u5206\u7a33\u5b9a\uff0c\u5177\u4f53\u8868\u73b0\u5728\u968f\u7740\u8fed\u4ee3\u6b21\u6570\u589e\u52a0\uff0cSEGNO\u7684\u8bef\u5dee\u589e\u957f\u66f2\u7ebf\u8f83\u5176\u4ed6\u6a21\u578b\u66f4\u4e3a\u5e73\u7f13\uff0c\u8bc1\u660e\u5176\u6bcf\u4e00\u6b65\u7684\u8bef\u5dee\u5747\u63a7\u5236\u5728\u8f83\u5c0f\u8303\u56f4\u3002<\/span><\/section>\n

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    <\/section>\n
    \u5728\u8bb8\u591a\u4e0b\u6e38\u4efb\u52a1\uff0c\u5982\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u548c\u4eba\u59ff\u6001\u4f30\u8ba1\u9884\u6d4b\u4e0a\uff0c\u6a21\u578b\u4e5f\u80fd\u8d85\u8fc7\u73b0\u6709\u65b9\u6cd5\uff0c\u5b9e\u73b0\u66f4\u597d\u7684\u6548\u679c\u3002<\/span><\/section>\n

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    <\/section>\n
    \u603b\u7ed3\u800c\u8a00\uff0cSEGNO\u6a21\u578b\u901a\u8fc7\u52a0\u5165\u4e24\u79cd\u5148\u9a8c\u5047\u8bbe\uff1a\u4e8c\u9636\u53c2\u91cf\u548c\u8fde\u7eed\u6027\u63d2\u503c\u9884\u6d4b\uff0c\u4ece\u800c\u66f4\u597d\u5730\u5efa\u6a21\u957f\u7a0b\u95ee\u9898\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u6a21\u578b\u80fd\u591f\u5e94\u7528\u4e8e\u5404\u79cdbackbone\u6a21\u578b\u4e0a\uff0c\u6cdb\u5316\u6027\u5f88\u5f3a\u3002<\/span><\/section>\n

    <\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    3.2 ESTAG\uff1aEquivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics[3]<\/span><\/strong><\/span>
    <\/section>\n

    <\/section>\n
    \u5728\u86cb\u767d\u8d28\u5efa\u6a21\u4e2d\uff0c\u7814\u7a76\u8005\u4eec\u901a\u5e38\u5173\u6ce8\u4e8e\u86cb\u767d\u8d28\u548c\u5c0f\u5206\u5b50\u7684\u7ed3\u6784\uff0c\u4ee5\u53ca\u5b83\u4eec\u5982\u4f55\u5b8c\u6210\u8bf8\u5982\u5bf9\u63a5\u8fd9\u6837\u7684\u52a8\u6001\u8fc7\u7a0b\u3002\u4f46\u5b9e\u9645\u4e0a\uff0c\u4e8c\u8005\u90fd\u5728\u540c\u4e00\u6eb6\u5242\u4e2d\u8fdb\u884c\u53cd\u5e94\uff0c\u6eb6\u5242\u5206\u5b50\u662f\u4e0d\u53ef\u5ffd\u89c6\u7684\u4e00\u79cd\u89d2\u8272\u3002\u6eb6\u5242\u5206\u5b50\u901a\u8fc7\u4e0e\u914d\u4f53\u3001\u53d7\u4f53\u8fdb\u884c\u4e92\u4f5c\uff0c\u6709\u53ef\u80fd\u5f71\u54cd\u5bf9\u63a5\u8fc7\u7a0b\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u5982\u679c\u91c7\u7528\u6570\u5b66\u8bed\u8a00\u7cbe\u70bc\u5316\u63cf\u8ff0\u4e0a\u8ff0\u60c5\u51b5\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba4\u4e3a\uff0c\u4e4b\u524d\u6240\u8ff0\u7684\u7406\u60f3\u6a21\u578b\u6ee1\u8db3\u9a6c\u5c14\u79d1\u592b\u94fe\u7ed3\u6784\u2014\u2014\u4efb\u610ft\u65f6\u523b\u7684\u5206\u5b50\u72b6\u6001\u56fe\u53ea\u548c\u5176\u524d\u4e00\u65f6\u523bt-1\u72b6\u6001\u56fe\u6709\u5173\u3002\u7136\u800c\uff0c\u5b9e\u9645\u60c5\u51b5\u4e0d\u6ee1\u8db3\u9a6c\u5c14\u79d1\u592b\u6027\u8d28\uff0ct\u65f6\u523b\u7684\u56fe\u548ct-1\u3001t-2\u4e43\u81f3t-3\u65f6\u523b\u72b6\u6001\u56fe\u6709\u5173\uff0c\u6b64\u5916\uff0c\u4e0d\u540c\u4f4d\u7f6e\u7684\u7c92\u5b50\u4e4b\u95f4\u4e5f\u4e92\u6709\u4f5c\u7528\u3002\u603b\u4e4b\uff0c\u8981\u8003\u8651\u65f6\u7a7a\u4f9d\u8d56\u6027\u8fdb\u800c\u5bf9\u5206\u5b50\u72b6\u6001\u8fdb\u884c\u7cbe\u786e\u4f30\u8ba1\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u9274\u4e8e\u6b64\uff0c\u6211\u4eec\u5f00\u53d1\u4e86\u4e00\u79cd\u878d\u5408\u65f6\u7a7a\u4fe1\u606f\u7684\u7b49\u53d8\u56fe\u795e\u7ecf\u7f51\u7edc\u6846\u67b6ESTAG\uff0c\u7528\u4e8e\u8fdb\u884c\u7c92\u5b50\u6a21\u62df\u3002\u6b64\u6846\u67b6\u5206\u4e3a\u56db\u4e2a\u90e8\u5206\uff1a<\/span><\/section>\n

    1. EDFT\uff1a\u4f7f\u7528\u79bb\u6563\u5085\u91cc\u53f6\u53d8\u6362\u5c06\u65f6\u57df\u4fe1\u606f\u8f6c\u5316\u6210\u9891\u57df\u4fe1\u606f\uff1b<\/span><\/p>\n

    2. ESM\uff1a\u878d\u5408\u7a7a\u95f4\u4fe1\u606f\u7684\u6d88\u606f\u4f20\u9012\u6a21\u5757\uff1b<\/span><\/p>\n

    3. ETM\uff1a\u878d\u5408\u65f6\u95f4\u4fe1\u606f\u548c\u6ce8\u610f\u529b\u673a\u5236\u7684\u6d88\u606f\u4f20\u9012\u6a21\u5757\uff1b<\/span><\/p>\n

    4. ETP\uff1aPooling\u6a21\u5757\uff0c\u5c06\u9891\u57df\u8f6c\u5316\u56de\u65f6\u57df<\/span>\u3002<\/section>\n

    <\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u4e0b\u9762\u4e24\u5f20\u56fe\u5927\u4f53\u6982\u62ec\u4e86\u65f6\u7a7a\u4f20\u9012\u673a\u5236\uff0c\u5176\u5b9e\u548cEGNN\u7684\u65b9\u5f0f\u5927\u4f53\u76f8\u5f53\uff0c\u53ea\u4e0d\u8fc7\u5728\u65f6\u95f4\u4fe1\u606f\u4f20\u9012\u65f6\u91c7\u7528\u4e86\u6ce8\u610f\u529b\u673a\u5236\uff0c\u5bf9\u4ece0\u65f6\u523b\u5f00\u59cb\u7684\u72b6\u6001<\/span>\uff08\u5750\u6807\uff0c\u901f\u5ea6\uff09<\/span>\u8fdb\u884c\u52a0\u6743\u6c42\u548c\uff0c\u968f\u540e\u751f\u6210\u4fe1\u606f\u7528\u4e8e\u4e0b\u4e00\u6b21\u4f20\u9012\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u901a\u8fc7\u5728\u5206\u5b50\u6a21\u62df\u6570\u636e\u96c6MD17\u4e0a\u7684\u5b9e\u9a8c\uff0c\u6211\u4eec\u8bc1\u5b9e\u4e86\u878d\u5408\u65f6\u7a7a\u4fe1\u606f\u786e\u5b9e\u80fd\u591f\u8ba9\u6a21\u62df\u6027\u80fd\u63d0\u5347\u4e00\u4e2a\u6570\u91cf\u7ea7\uff0c\u4ece\u66f4\u5b8f\u89c2\u7684\u86cb\u767d\u6570\u636e\u96c6\u4e0a\u4e5f\u6536\u83b7\u5230\u4e86\u7c7b\u4f3c\u7684\u6548\u679c\u3002\u53ef\u89c6\u5316\u5b9e\u9a8c\u4e5f\u8bc1\u660e\u4e86ESTAG\u80fd\u591f\u5728\u9884\u6d4b\u8fc7\u7a0b\u4e2d\u4fdd\u7559EGNN\u4e22\u5931\u7684\u5316\u5b66\u952e\uff0c\u5e76\u80fd\u66f4\u7cbe\u786e\u5730\u751f\u6210\u86cb\u767d\u8d28alpha\u6298\u53e0\u548c\u4eba\u4f53\u59ff\u6001\u6570\u636e\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n
    <\/section>\n

    <\/section>\n
    \u603b\u7ed3\u800c\u8a00\uff0cESTAG\u6a21\u578b\u878d\u5408\u65f6\u7a7a\u4fe1\u606f\u8fdb\u884c\u6d88\u606f\u4f20\u9012\uff0c\u8fdb\u800c\u63d0\u9ad8\u7c92\u5b50\u6a21\u62df\u6027\u80fd\u3002\u8be5\u8bba\u6587\u4e5f\u4e2d\u7a3f\u4e86NeurIPS 2023\u5e74\u7684poster\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n

    <\/section>\n
    3.3 DEGNN: Discrete Equivariant Graph Neural Network[4]<\/span><\/strong><\/span>
    <\/section>\n

    <\/span><\/section>\n
    \u51e0\u4f55\u6df1\u5ea6\u5b66\u4e60\u7684\u7b49\u53d8\u6027\u7ea6\u675f\u5df2\u7ecf\u5e7f\u4e3a\u4eba\u77e5\uff0c\u5e76\u5e94\u7528\u5230\u5404\u4e2a\u9886\u57df\u3002\u7136\u800c\uff0c\u5bf9\u6240\u6709\u7cfb\u7edf\u90fd\u52a0\u4ee5\u7b49\u53d8\u6027\u7ea6\u675f\u662f\u5426\u592a\u8fc7\u4e25\u683c\uff1f\u4f8b\u5982\uff0c\u9ad8\u901f\u516c\u8def\u4e0a\u7684\u6c7d\u8f66\u6c38\u8fdc\u4e0d\u4f1a\u8dd1\u51fa\u8def\uff0c\u56e0\u6b64\u5b83\u7684\u7b49\u53d8\u6027\u53ea\u4f53\u73b0\u5728\u65cb\u8f6c180\u5ea6\u540e\u6709\u6548\u3002\u56e0\u6b64\uff0c\u6709\u5fc5\u8981\u63a2\u7a76\u5728\u4e0d\u540c\u7684\u79bb\u6563\u7b49\u53d8\u7fa4\u4e0a\u9002\u5f53\u653e\u677e\u7b49\u53d8\u6027\u7ea6\u675f\uff0c\u662f\u5426\u66f4\u4e3a\u5408\u7406\uff1f<\/span><\/section>\n

    <\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u501f\u9274\u6676\u4f53\u91cc\u9762\u7684\u6982\u5ff5\uff0c\u6211\u4eec\u9996\u5148\u5b9a\u4e49\u70b9\u7fa4<\/span>\uff08point group\uff09<\/span>\u3002\u70b9\u7fa4\u662f\u4f20\u7edf\u7b49\u53d8\u6027\u7684\u653e\u677e\u7ea6\u675f\u5f62\u5f0f\uff0c\u5b83\u7531\u4e00\u7fa4\u79bb\u6563\u64cd\u4f5c\u7ec4\u6210\uff0c\u8fd9\u4e9b\u64cd\u4f5c\u4f7f\u7c92\u5b50\u4fdd\u6301\u79bb\u6563\u7b49\u53d8\u6027\u3002\u4e3e\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff0cD4\u7b49\u53d8\u7fa4[7]\u5305\u542b\u4e86\u5c06\u7c92\u5b50\u65cb\u8f6c90\u3001180\u3001270\u3001360\u5ea6\u8fd9\u56db\u79cd\u7b49\u53d8\u6027\u64cd\u4f5c\u3002\u6211\u4eec\u6709\u4e86\u79bb\u6563\u7b49\u53d8\u7fa4\u4e4b\u540e\uff0c\u4e0b\u4e00\u6b65\u7684\u5173\u952e\u5c31\u662f\u5982\u4f55\u6784\u9020\u51fd\u6570\uff0c\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u5b9e\u73b0\u79bb\u6563\u7b49\u53d8\u64cd\u4f5c\u3002<\/span><\/section>\n

    <\/section>\n
    \u4e0b\u9762\u662f\u4e00\u4e2a\u6784\u9020\u65b9\u5f0f\u03bcp\uff0c<\/sub>\u5b83\u65e2\u80fd\u4fdd\u6301\u6392\u5217\u4e0d\u53d8\u6027<\/span>\uff08\u8f93\u5165\u6570\u636e\u987a\u5e8f\u4e0d\u540c\u5176\u8f93\u51fa\u7ed3\u679c\u76f8\u540c\uff09<\/span>\uff0c\u53c8\u80fd\u4fdd\u6301\u79bb\u6563\u7b49\u53d8\u6027\u3002\u5bf9\u6bd4\u548cEGNN\u7684\u533a\u522b\uff0c\u53ef\u53d1\u73b0\u5176\u6838\u5fc3\u662f\u5c06EGNN\u6a21\u578b\u03c3\u4e2d\u4ece\u5411\u91cf\u8f6c\u5316\u4e3a\u6807\u91cf\u8fd9\u4e2a\u7ea6\u675f\u8fdb\u884c\u4e86\u653e\u677e\uff0c\u5c06\u7ea6\u675f\u8f6c\u79fb\u5230\u03c6\u4e0a\uff0c\u53ea\u8981\u03c6\u6ee1\u8db3\u6392\u5217\u4e0d\u53d8\u6027\uff0c\u90a3\u4e48\u5bf9\u4e8e\u4efb\u610f\u7ed9\u5b9a\u7684\u70b9\u7fa4\uff0c\u4e0b\u9762\u7684\u6784\u9020\u90fd\u53ef\u4ee5\u6ee1\u8db3\u79bb\u6563\u7b49\u53d8\u6027\u3002\u6240\u4ee5x\u7684\u5bf9\u79f0\u6027\u7834\u7f3a\u7279\u5f81<\/span>\uff08symmetry-breaking feature\uff09<\/span>\u5176\u5b9e\u5c31\u662f\u8868\u660ex\u4e0d\u7528\u662f\u6807\u91cf\u4e86\uff0c\u800c\u53ef\u4ee5\u662f\u4e00\u5806\u5411\u91cf\u4e5f\u6ee1\u8db3\u6761\u4ef6\u3002\u56e0\u6b64\uff0c\u03bcp<\/sub>\u662fEGNN\u7684\u4e00\u822c\u6027\u8868\u793a\u3002<\/span><\/section>\n

    <\/section>\n
    <\/section>\n

    <\/section>\n
    \u6211\u4eec\u91c7\u7528\u8fd9\u6837\u7684\u6784\u9020\u65b9\u5f0f\u642d\u5efa\u6a21\u578b\uff0c\u5e76\u8fdb\u884c\u4e86\u4ece\u5b8f\u89c2\u5230\u5fae\u89c2\u7684\u4e0d\u540c\u7269\u7406\u7cfb\u7edf<\/span>\uff08\u5982\u9ad8\u901f\u516c\u8def\u3001\u5206\u5b50\u52a8\u529b\u5b66\uff09<\/span>\u7684\u5b9e\u9a8c\u3002\u7ed3\u679c\u8bc1\u660e\uff0c\u6a21\u578b\u5728\u5404\u4e2a\u9886\u57df\u7684\u5b9e\u9a8c\u5747\u8868\u73b0\u4f18\u5f02\uff0c\u4ece\u800c\u8bc1\u5b9e\u672c\u7814\u7a76\u5f00\u5934\u63d0\u5230\u7684\u5047\u8bbe\uff1a\u7b49\u53d8\u6027\u7ea6\u675f\u5f80\u5f80\u592a\u8fc7\u4e25\u683c\uff0c\u9002\u5f53\u653e\u677e\u7ea6\u675f\u4e5f\u662f\u4e0d\u9519\u7684\u9009\u62e9\uff0c\u4e3a\u672a\u6765\u6a21\u578b\u8bbe\u8ba1\u548c\u7814\u7a76\u63d0\u4f9b\u4e86\u53c2\u8003\u3002<\/span><\/section>\n

    <\/section>\n
    <\/section>\n

    <\/section>\n
    \u8fd9\u7bc7\u6587\u7ae0\u540c\u65f6\u4e5f\u4e2d\u7a3fKDD 2024\uff0c\u7528\u6784\u9020\u51fd\u6570\u7684\u65b9\u5f0f\u5411\u7814\u7a76\u8005\u4eec\u5c55\u793a\u653e\u677e\u7b49\u53d8\u6027\u7ea6\u675f\u4e5f\u4e0d\u5931\u4e3a\u4e00\u79cd\u6709\u6548\u529e\u6cd5\u3002<\/span><\/section>\n

    <\/section>\n
    <\/section>\n


    <\/p>\n


    <\/section>\n

    \n
    \n
    \n
    \n
    \n

    <\/section>\n

    <\/section>\n
    \n
    <\/svg><\/section>\n<\/section>\n<\/section>\n
    \n

    4. \u9488\u5bf9\u590d\u6742\u7cfb\u7edf\u7684\u5efa\u6a21<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

    \n

    <\/section>\n

    <\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


    <\/p>\n

    \u4ee5HGIN: Geometric Graph Learning for Protein Mutation Effect Prediction[5]\u4e3a\u4f8b\uff0c\u6211\u4eec\u5c55\u793a\u7b49\u53d8\u56fe\u795e\u7ecf\u7f51\u7edc\u5728\u590d\u6742\u7cfb\u7edf\u5efa\u6a21\u4e0a\u7684\u5e94\u7528\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    \u5728\u86cb\u767d\u8d28\u5efa\u6a21\u4efb\u52a1\u4e2d\uff0c\u6709\u4e00\u79cd\u4efb\u52a1\u79f0\u4e4b\u4e3a\u5b9a\u5411\u8fdb\u5316\u3002\u5176\u672c\u8d28\u662f\u901a\u8fc7\u7a81\u53d8\uff0c\u63a2\u7a76\u86cb\u767d\u4eb2\u548c\u529b\u7684\u53d8\u5316\uff0c\u5982\u679c\u4eb2\u548c\u529b\u589e\u52a0\uff0c\u5219\u4fdd\u7559\u8fd9\u4e2a\u7a81\u53d8\uff0c\u5728\u8be5\u7a81\u53d8\u57fa\u7840\u4e0a\u7ee7\u7eed\u7a81\u53d8\uff0c\u4ece\u800c\u8fed\u4ee3\u6539\u8fdb\u86cb\u767d\u8d28\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n

    <\/span><\/section>\n
    \u5df2\u6709\u5de5\u4f5c\u91c7\u7528\u5e8f\u5217\u8f93\u5165\u7684\u65b9\u5f0f\uff0c\u7528\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\/\u5927\u8bed\u8a00\u6a21\u578b\u8fdb\u884c\u5efa\u6a21\uff1b\u6b64\u5916\u8fd8\u6709\u901a\u8fc7\u5377\u79ef\u6838\u5377\u79ef\u86cb\u767d\u4e09\u7ef4\u7ed3\u6784\u5bf9\u7279\u5f81\u8fdb\u884c\u6c47\u603b\u3002\u5b58\u5728\u4e24\u4e2a\u6311\u6218\uff1a1\uff09\u672a\u8003\u8651\u4e0d\u540c\u5c42\u6b21\u7684\u7279\u5f81\uff1a\u7531\u4e8e\u662f\u5355\u6c28\u57fa\u9178\u7a81\u53d8\uff0c\u5982\u679c\u4ece\u6c28\u57fa\u9178\u5e8f\u5217\u5c42\u9762\u6765\u770b\uff0c\u5e8f\u5217\u53d8\u5316\u5341\u5206\u5fae\u5c0f\uff0c\u53ef\u80fd\u5728\u6a21\u578b\u5b66\u4e60\u8fc7\u7a0b\u4e2d\u65e0\u6cd5\u5b66\u4e60\u51fa\u5dee\u5f02\u4fe1\u606f\uff1b\u7136\u800c\u4ece\u539f\u5b50\u5c42\u9762\u4e0a\u770b\uff0c\u7a81\u53d8\u4e00\u4e2a\u6c28\u57fa\u9178\u4ea7\u751f\u7684\u5f71\u54cd\u6bd4\u8f83\u5927\u2014\u2014\u56e0\u6b64\u8981\u878d\u5408\u4e0d\u540c\u5c42\u6b21\u7684\u4fe1\u606f\u30022\uff09\u5728\u4eb2\u548c\u529b\u9884\u6d4b\u65f6\uff0c\u8981\u6ee1\u8db3\u7b49\u53d8\u6027\u3002<\/span><\/section>\n

    <\/span><\/section>\n
    <\/section>\n
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    \u4e3a\u6b64\uff0c\u6211\u4eec\u8bbe\u8ba1\u4e86HGIN\u2014\u2014\u5c42\u6b21\u7b49\u53d8\u56fe\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002\u6a21\u578b\u540c\u65f6\u8f93\u5165\u91ce\u751f\u578b\u548c\u7a81\u53d8\u578b\u7ed3\u6784\uff0c\u9996\u5148\u4ece\u539f\u5b50\u5c42\u9762\u8fdb\u884c\u6d88\u606f\u805a\u5408\uff0c\u968f\u540e\u901a\u8fc7\u6c60\u5316<\/span>\uff08Pooling\uff09<\/span>\u65b9\u5f0f\u5c06\u5176\u6c47\u603b\u5230\u6bcf\u4e2a\u6c28\u57fa\u9178\u7684\u7279\u5f81\uff1b\u5728\u6c28\u57fa\u9178\u5c42\u9762\uff0c\u8003\u8651\u6c28\u57fa\u9178\u4e4b\u95f4\u7684\u7279\u5f81\u76f8\u4f3c\u6027\u3001\u7a7a\u95f4\u4e0a\u662f\u5426\u76f8\u8fd1\u3001\u5728\u5e8f\u5217\u4e0a\u7684\u4f4d\u7f6e\u7b49\u591a\u89d2\u5ea6\u4fe1\u606f\u8fdb\u884c\u6ce8\u610f\u529b\u673a\u5236\u52a0\u6743\u5e73\u5747\u6c42\u548c\uff0c\u6700\u540e\u9884\u6d4b\u51fa\u4e24\u4e2a\u6f5c\u5728\u8868\u793a\uff0c\u52a0\u4e0aMLP\u5c42\u9884\u6d4b\u4eb2\u548c\u529b\u53d8\u5316\u60c5\u51b5\uff0c\u540c\u539f\u6765\u5df2\u77e5\u7684\u6807\u7b7e\u8fdb\u884c\u76d1\u7763\u5b66\u4e60\u3002<\/span><\/section>\n

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    5. \u672a\u6765\u5c55\u671b<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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    <\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


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    \u79d1\u5b66\u7814\u7a76\u7684\u8303\u5f0f\u53ef\u5206\u4e3a\u4e24\u7c7b\uff1a\u4e00\u7c7b\u662f\u5f00\u666e\u52d2\u7684\u6570\u636e\u9a71\u52a8\u578b\u2014\u2014\u7ed9\u5b9a\u5927\u6279\u6570\u636e\uff0c\u901a\u8fc7AI\u6a21\u578b\u5b66\u4e60\u5e95\u5c42\u89c4\u5f8b\uff1b\u53e6\u4e00\u7c7b\u662f\u725b\u987f\u7684\u6a21\u578b\u9a71\u52a8\u578b\u2014\u2014\u4ece\u7b2c\u4e00\u6027\u539f\u7406\u5f00\u59cb\u63a8\u5bfc\uff0c\u5efa\u7acb\u7406\u8bba\u6846\u67b6\u3002\u5728\u5f53\u4eca\u7684\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u662f\u5426\u80fd\u5c06\u8fd9\u4e24\u79cd\u8303\u5f0f\u8fdb\u884c\u7ed3\u5408\u5462\uff1f\u80fd\u5426\u901a\u8fc7\u6ce8\u5165\u7269\u7406\u5148\u9a8c\u77e5\u8bc6\uff0c\u5728\u6709\u9650\u7684\u6570\u636e\u4e0b\u8bad\u7ec3\u51fa\u4e00\u4e2a\u201c\u8db3\u591f\u806a\u660e\u201d\u7684\u5927\u6a21\u578b\u5462\uff1f\u503c\u5f97\u5927\u5bb6\u601d\u8003\u3002<\/span><\/section>\n

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    AI for Science\u9886\u57df\u7684\u5927\u6a21\u578b\u5e94\u8be5\u600e\u6837\u505a\uff1f\u5728\u6211\u770b\u6765\uff0c\u79d1\u5b66\u95ee\u9898\u6db5\u76d6\u7684\u9886\u57df\u975e\u5e38\u5e7f\u6cdb\uff0c\u4ece\u751f\u7269\u5230\u7269\u7406\u3002\u5176\u95ee\u9898\u7684\u5c3a\u5ea6\u548c\u590d\u6742\u6027\u4e5f\u5404\u6709\u4e0d\u540c\uff0c\u5927\u5230\u5929\u4f53\uff0c\u5c0f\u5230\u5fae\u7c92\u3002\u5728\u8fc7\u53bb\u6570\u767e\u5e74\u7684\u7814\u7a76\u4e2d\uff0c\u79d1\u5b66\u5bb6\u4eec\u5df2\u7ecf\u79ef\u6512\u4e86\u975e\u5e38\u4e30\u5bcc\u7684\u591a\u5c3a\u5ea6\u6570\u636e\uff0c\u90a3\u4e48\u6211\u4eec\u80fd\u5426\u901a\u8fc7\u878d\u5165\u4e00\u5b9a\u7684\u5148\u9a8c\u77e5\u8bc6\uff0c\u7814\u53d1\u51fa\u591a\u6a21\u6001\u3001\u591a\u5c3a\u5ea6\u5927\u6a21\u578b\uff0c\u4ece\u800c\u6253\u901a\u5404\u4e2a\u9886\u57df\uff0c\u52a9\u529b\u79d1\u5b66\u53d1\u73b0\u5462? \u5982\u679c\u80fd\u5b9e\u73b0\uff0c\u90a3\u4e48\u6211\u4eec\u5c31\u53ef\u80fd\u901a\u8fc7\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u53bb\u9884\u6d4b\u86cb\u767d\u7ed3\u6784\uff0c\u901a\u8fc7\u86cb\u767d\u8d28\u7ed3\u6784\u9884\u6d4b\u50ac\u5316\u7cfb\u7edf\u7684\u7ed3\u5408\u80fd\u3002<\/span><\/section>\n

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    \u6211\u4eec\u6709\u5f3a\u5927\u7684\u7b97\u529b\u548c\u6027\u80fd\u4f18\u5f02\u7684\u7b97\u6cd5\uff0c\u6211\u4eec\u4e5f\u6709\u957f\u4e45\u79ef\u7d2f\u4e0b\u6765\u7684\u7269\u7406\u5b66\u5b9a\u5f8b\uff0c\u5982\u4f55\u7528\u8fd9\u4e24\u5757\u9762\u5305\uff0c\u89e3\u51b3\u5f88\u591a\u91cd\u8981\u7684\u79d1\u5b66\u95ee\u9898<\/span>\uff08\u91cc\u9762\u7684\u9e21\u6392\uff09<\/span>\uff0c\u4ece\u800c\u505a\u597d\u8fd9\u4e2a\u6c49\u5821\u5305\uff0c\u83b7\u5f97\u65b0\u7684\u79d1\u5b66\u8303\u5f0f\uff0c\u662f\u5404\u4f4d\u7814\u7a76\u8005\u4eec\u672a\u6765\u594b\u6597\u7684\u76ee\u6807\u3002<\/span><\/section>\n

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    \u5c55\u671b\u672a\u6765\uff0cAI for Science\u7684\u57fa\u7840\u6a21\u578b\u53ef\u4ee5\u901a\u8fc7\u5927\u8bed\u8a00\u6a21\u578b\u5916\u52a0\u77e5\u8bc6\u5e93\u7684\u5f62\u5f0f\u8fdb\u884c\u53d1\u5c55\uff0c\u4e5f\u53ef\u6253\u9020\u4e00\u6279\u4e13\u4e1a\u9886\u57df\u6a21\u578b\u4f9b\u5927\u8bed\u8a00\u6a21\u578b\u8fdb\u884c\u8c03\u7528\uff0c\u8fd9\u4e9b\u53ef\u80fd\u90fd\u4f1a\u5728\u4e0d\u8fdc\u7684\u5c06\u6765\u5b9e\u73b0\uff0c\u672a\u6765\u53ef\u671f\uff01<\/span><\/section>\n

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    \u53c2\u8003\u6587\u732e\uff1a<\/strong><\/span><\/section>\n
    [1] Satorras, V\u0131ctor Garcia, Emiel Hoogeboom, and Max Welling. "E (n) equivariant graph neural networks." International conference on machine learning. PMLR, 2021<\/span><\/section>\n
    [2] Liu, Yang, et al. "SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases." The Twelfth International Conference on Learning Representations, 2024, https:\/\/openreview.net\/forum?id=3oTPsORaDH.<\/span><\/section>\n
    [3] Wu, Liming, et al. "Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics." Thirty-seventh Conference on Neural Information Processing Systems, 2023, https:\/\/openreview.net\/forum?id=35nFSbEBks.<\/span><\/section>\n
    [4] Zheng, Zinan, et al. "Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning." ArXiv, 2024, https:\/\/arxiv.org\/abs\/2406.16295.<\/span><\/section>\n
    [5] Zhao, Kangfei, et al. "Geometric Graph Learning for Protein Mutation Effect Prediction." Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, ACM, 2023, pp. 3412\u20133422, https:\/\/doi.org\/10.1145\/3583780.3614893<\/span><\/section>\n
    [6] Han, Jiaqi, et al. "A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications." arXiv, 2024, https:\/\/arxiv.org\/abs\/2403.00485.<\/span><\/section>\n
    [7] Mondal, Arnab Kumar, et al. "Group Equivariant Deep Reinforcement Learning." arXiv preprint arXiv, 2020, https:\/\/arxiv.org\/abs\/2007.03437.<\/span><\/section>\n

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    https:\/\/pattern.swarma.org\/study_group_issue\/737<\/span><\/section>\n

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    \u51e0\u4f55\u6df1\u5ea6\u5b66\u4e60\u8bfb\u4e66\u4f1a<\/strong>
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    \u62d3\u6251\u7f16\u7ec7\u7740\u590d\u6742\u4e16\u754c\uff0c\u673a\u5668\u5b66\u4e60\u5b55\u80b2\u7740\u6280\u672f\u5947\u70b9\u3002\u4e00\u4e2a\u7ef4\u5ea6\uff0c\u5176\u4e2d\u62d3\u6251\u7406\u8bba\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4ea4\u7ec7\u5171\u9e23\uff1b\u4e00\u4e2a\u9886\u57df\uff0c\u5b83\u8de8\u8d8a\u4e86\u6570\u5b66\u7684\u4e25\u8c28\u4e0e\u672c\u8d28\u4ee5\u53ca\u4eba\u5de5\u667a\u80fd\u7684\u65e0\u9650\u53ef\u80fd\uff0c\u5f00\u8f9f\u7740\u901a\u5f80\u79d1\u5b66\u65b0\u7eaa\u5143\u7684\u822a\u9053\u3002\u8ba9\u6211\u4eec\u643a\u624b\u5728\u51e0\u4f55\u6df1\u5ea6\u5b66\u4e60\u7684\u8d77\u70b9\u51fa\u53d1\uff0c\u4e00\u8def\u63a2\u7d22\u5982\u4f55\u8d70\u5411AI for Science\u7684\u65e0\u9650\u672a\u6765\u3002<\/span><\/p>\n


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    \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u590d\u65e6\u5927\u5b66\u667a\u80fd\u590d\u6742\u4f53\u7cfb\u5b9e\u9a8c\u5ba4\u9752\u5e74\u7814\u7a76\u5458\u6731\u7fa4\u559c\u3001\u6d59\u6c5f\u5927\u5b66\u767e\u4eba\u8ba1\u5212\u7814\u7a76\u5458\u674e\u6a35\u98ce\u3001\u6e05\u534e\u5927\u5b66\u7535\u5b50\u5de5\u7a0b\u7cfb\u6570\u636e\u79d1\u5b66\u4e0e\u667a\u80fd\u5b9e\u9a8c\u5ba4\u535a\u58eb\u540e\u7814\u7a76\u5458\u4e01\u749f\u97ec\u3001\u7f8e\u56fd\u4e1c\u5317\u5927\u5b66\u7269\u7406\u7cfbAlbert-L\u00e1szl\u00f3 Barab\u00e1si\u6307\u5bfc\u7684\u535a\u58eb\u540e\u9ad8\u5a77\u5a77\u3001\u5317\u4eac\u5927\u5b66\u535a\u96c5\u535a\u58eb\u540e\u66f9\u6587\u797a\u3001\u590d\u65e6\u5927\u5b66\u6570\u5b66\u79d1\u5b66\u5b66\u9662\u5e94\u7528\u6570\u5b66\u65b9\u5411\u535a\u58eb\u7814\u7a76\u751f\u8d75\u4f2f\u6797\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7cfb\u7edf\u79d1\u5b66\u5b66\u9662\u535a\u58eb\u7814\u7a76\u751f\u725f\u7267\u4e91\uff0c\u5171\u540c\u53d1\u8d77\u300c\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u300d\u8bfb\u4e66\u4f1a\u7b2c\u4e8c\u5b63<\/a>\u3002<\/span><\/p>\n


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    \u8bfb\u4e66\u4f1a\u5c06\u4e8e9\u67087\u65e5\u6bcf\u5468\u516d\u665a\u4e0a20:00-22:00\u8fdb\u884c\uff0c\u63a2\u8ba8\u56db\u4e2a\u6838\u5fc3\u6a21\u5757\uff1a\u6570\u636e\u9a71\u52a8\u7684\u590d\u6742\u7cfb\u7edf\u5efa\u6a21\u3001\u590d\u6742\u7f51\u7edc\u7ed3\u6784\u63a8\u65ad\u3001\u5177\u6709\u53ef\u89e3\u91ca\u6027\u7684\u590d\u6742\u7cfb\u7edf\u63a8\u65ad\uff08\u52a8\u529b\u5b66+\u7f51\u7edc\u7ed3\u6784\uff09\u3001\u5e94\u7528-\u8d85\u6750\u6599\u8bbe\u8ba1\u548c\u57ce\u5e02\u7cfb\u7edf\uff0c\u901a\u8fc7\u91cd\u70b9\u8ba8\u8bba75\u7bc7\u7ecf\u5178\u3001\u524d\u6cbf\u7684\u91cd\u8981\u6587\u732e\uff0c\u4ece\u9ed1\u76d2\uff08\u6570\u636e\u9a71\u52a8\uff09\u5230\u767d\u76d2\uff08\u53ef\u89e3\u91ca<\/span>\u6027\uff09\uff0c\u9010\u6b65\u6355\u6349\u7cfb\u7edf\u7684\u201c\u672c\u8d28\u201d\u89c4\u5f8b\uff0c\u5e2e\u52a9\u5927\u5bb6\u66f4\u597d\u7684\u8ba4\u8bc6\u3001\u7406\u89e3\u3001\u9884\u6d4b\u3001\u63a7\u5236\u3001\u8bbe\u8ba1\u590d\u6742\u7cfb\u7edf\uff0c\u4e3a\u76f8\u5173\u9886\u57df\u7684\u7814\u7a76\u548c\u5e94\u7528\u63d0\u4f9b\u6d1e\u89c1\u3002\u6b22\u8fce\u611f\u5174\u8da3\u7684\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\uff01<\/span><\/p>\n

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    <\/span><\/a><\/section>\n

    <\/span><\/section>\n
    \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n

    \u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u8bfb\u4e66\u4f1a\uff1a\u4ece\u6570\u636e\u9a71\u52a8\u5230\u53ef\u89e3\u91ca\u6027\uff0c\u63a2\u7d22\u7cfb\u7edf\u5185\u5728\u89c4\u5f8b\uff5c\u5185\u964475\u7bc7\u9886\u57df\u5fc5\u8bfb\u6587\u732e<\/strong><\/a><\/p>\n


    <\/p>\n


    <\/p>\n

    \n
    \n
    \n
    \n

    AI+Science \u8bfb\u4e66\u4f1a<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    \n


    <\/p>\n<\/section>\n

    AI+Science \u662f\u8fd1\u5e74\u5174\u8d77\u7684\u5c06\u4eba\u5de5\u667a\u80fd\u548c\u79d1\u5b66\u76f8\u7ed3\u5408\u7684\u4e00\u79cd\u8d8b\u52bf\u3002\u4e00\u65b9\u9762\u662f AI for Science\uff0c\u673a\u5668\u5b66\u4e60\u548c\u5176\u4ed6 AI \u6280\u672f\u53ef\u4ee5\u7528\u6765\u89e3\u51b3\u79d1\u5b66\u7814\u7a76\u4e2d\u7684\u95ee\u9898\uff0c\u4ece\u9884\u6d4b\u5929\u6c14\u548c\u86cb\u767d\u8d28\u7ed3\u6784\uff0c\u5230\u6a21\u62df\u661f\u7cfb\u78b0\u649e\u3001\u8bbe\u8ba1\u4f18\u5316\u6838\u805a\u53d8\u53cd\u5e94\u5806\uff0c\u751a\u81f3\u50cf\u79d1\u5b66\u5bb6\u4e00\u6837\u8fdb\u884c\u79d1\u5b66\u53d1\u73b0\uff0c\u88ab\u79f0\u4e3a\u79d1\u5b66\u53d1\u73b0\u7684\u201c\u7b2c\u4e94\u8303\u5f0f\u201d\u3002\u53e6\u4e00\u65b9\u9762\u662f Science for AI\uff0c\u79d1\u5b66\u5c24\u5176\u662f\u7269\u7406\u5b66\u4e2d\u7684\u89c4\u5f8b\u548c\u601d\u60f3\u542f\u53d1\u673a\u5668\u5b66\u4e60\u7406\u8bba\uff0c\u4e3a\u4eba\u5de5\u667a\u80fd\u7684\u53d1\u5c55\u63d0\u4f9b\u5168\u65b0\u7684\u89c6\u89d2\u548c\u65b9\u6cd5\u3002<\/span><\/section>\n

    <\/section>\n
    \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u65af\u5766\u798f\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\u535a\u58eb\u540e\u7814\u7a76\u5458\u5434\u6cf0\u9716\uff08Jure Leskovec \u6559\u6388\u6307\u5bfc\uff09\u3001\u54c8\u4f5b\u91cf\u5b50\u8ba1\u5212\u7814\u7a76\u5458\u6248\u9e3f\u4e1a\u3001\u9ebb\u7701\u7406\u5de5\u5b66\u9662\u7269\u7406\u7cfb\u535a\u58eb\u751f\u5218\u5b50\u9e23\uff08Max Tegmark \u6559\u6388\u6307\u5bfc\uff09\uff0c\u5171\u540c\u53d1\u8d77\u4ee5\u201cAI+Science<\/a>\u201d\u4e3a\u4e3b\u9898\u7684\u8bfb\u4e66\u4f1a\uff0c\u63a2\u8ba8\u8be5\u9886\u57df\u7684\u91cd\u8981\u95ee\u9898\uff0c\u5171\u5b66\u5171\u7814\u76f8\u5173\u6587\u732e\u3002\u8bfb\u4e66\u4f1a\u5df2\u5b8c\u7ed3\uff0c\u73b0\u5728\u62a5\u540d\u53ef\u52a0\u5165\u793e\u7fa4\u5e76\u89e3\u9501\u56de\u653e\u89c6\u9891\u6743\u9650\u3002<\/span><\/span><\/section>\n

    <\/section>\n

    <\/span><\/a><\/p>\n


    <\/p>\n

    \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
    \u4eba\u5de5\u667a\u80fd\u548c\u79d1\u5b66\u53d1\u73b0\u76f8\u4e92\u8d4b\u80fd\u7684\u65b0\u8303\u5f0f\uff1aAI+Science \u8bfb\u4e66\u4f1a\u542f\u52a8<\/a><\/span><\/section>\n

    <\/section>\n

    <\/section>\n
    \u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/strong><\/section>\n
    1. <\/strong>\u4ece\u56fe\u795e\u7ecf\u7f51\u7edc\u5230\u51e0\u4f55\u6df1\u5ea6\u5b66\u4e60\uff1aAI4Science\u7684\u4e0b\u4e00\u4e2a\u98a0\u8986\u6027\u7a81\u7834\u5728\u4f55\u5904\uff1f<\/a><\/strong><\/span><\/strong><\/section>\n
    2. <\/strong>\u524d\u6cbf\u63a2\u8ba8\uff1a\u56fe\u795e\u7ecf\u7f51\u7edc\u3001\u7ec4\u5408\u4f18\u5316\u3001\u5927\u6a21\u578b\u4e0eAI4Science\u5982\u4f55\u76f8\u4e92\u4fc3\u8fdb\uff1f<\/a><\/strong><\/span><\/section>\n
    3. <\/strong>\u51e0\u4f55\u6df1\u5ea6\u5b66\u4e60\uff1a\u8ba9\u7269\u7406\u4e16\u754c\u62e5\u6709AI | \u9ec4\u6587\u70b3\u5206\u4eab\u6574\u7406<\/a><\/strong><\/span><\/section>\n
    <\/a><\/section>\n
    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> \u9f99\u5e74\u5927\u8fd0\u8d77\uff0c\u5b66\u4e60\u6b63\u5f53\u65f6\uff01\u89e3\u9501\u96c6\u667a\u5168\u7ad9\u5185\u5bb9\uff0c\u5f00\u542f\u65b0\u5e74\u5b66\u4e60\u8ba1\u5212<\/a><\/strong><\/strong><\/strong><\/strong><\/span><\/strong><\/strong><\/section>\n

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


    <\/p>\n


    <\/span><\/p>\n

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