{"id":52144,"date":"2024-08-20T18:47:09","date_gmt":"2024-08-20T10:47:09","guid":{"rendered":"https:\/\/swarma.org\/?p=52144"},"modified":"2024-08-20T18:47:09","modified_gmt":"2024-08-20T10:47:09","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-%e4%bb%8e%e5%8a%a8%e6%80%81%e6%a8%a1%e6%8b%9f%e5%88%b0","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=52144","title":{"rendered":"\u57fa\u4e8e\u51e0\u4f55\u56fe\u5b66\u4e60\u7684\u7269\u7406\u7cfb\u7edf\u5efa\u6a21\u2014\u2014\u4ece\u52a8\u6001\u6a21\u62df\u5230\u590d\u6742\u7cfb\u7edf\u5efa\u6a21\u4e28\u51e0\u4f55\u6df1\u5ea6\u5b66\u4e60\u00b7\u5468\u56db\u76f4\u64ad"},"content":{"rendered":"
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\u300c\u51e0\u4f55\u6df1\u5ea6\u5b66\u4e60\u300d\u8bfb\u4e66\u4f1a<\/span><\/strong><\/a>\u7b2c\u516d\u671f\u5c06\u7531\u963f\u91cc\u5df4\u5df4\u8fbe\u6469\u9662\u8d44\u6df1\u7b97\u6cd5\u5de5\u7a0b\u5e08\u8363\u94b0\uff0c\u9488\u5bf9\u590d\u6742\u7269\u7406\u7cfb\u7edf\u548c\u957f\u65f6\u95f4\u52a8\u6001\u6a21\u62df\u7cfb\u7edf\uff0c\u4ecb\u7ecd\u5176\u56e2\u961f\u57fa\u4e8e\u51e0\u4f55\u56fe\u5b66\u4e60\u5bf9\u8fd9\u4e24\u7c7b\u7cfb\u7edf\u7684\u5efa\u6a21\u6700\u65b0\u5de5\u4f5c\u4ee5\u53ca\u76f8\u5173\u5e94\u7528\uff0c\u5e76\u4e14\u5bf9\u672a\u6765AI for Science\u76f8\u5173\u9886\u57df\u8fdb\u884c\u5c55\u671b\u3002\u8bfb\u4e66\u4f1a\u5c06\u4e8e8\u670822\u65e5\uff08\u672c\u5468\u56db\uff09\u8fdb\u884c\uff0c\u6b22\u8fce\u611f\u5174\u8da3\u7684\u670b\u53cb\u53c2\u4e0e\u8ba8\u8bba\u4ea4\u6d41\uff01<\/span><\/strong><\/span><\/strong><\/section>\n
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Geometric Graph Learning for Modeling Physical Systems: From Dynamic Systems to Complex Systems<\/strong><\/span><\/section>\n

<\/strong><\/span><\/section>\n
In current fundamental scientific research, the vast majority of tasks can essentially be reduced to describing and modeling different physical systems. For instance, predicting the structure of proteins allows us to understand their functions, simulating molecular dynamics enables us to better comprehend the mechanisms of chemical reactions, and predicting the binding energies of systems can help us screen for better catalysts. Recent advancements in deep learning, especially through the adoption of graph neural networks, have led to the application of a growing number of models across a spectrum of physical system from subatomic to macromolecular scales\u2014 achieving remarkable progress. In this presentation, I will introduce our latest work and relevant applications in modeling complex physical systems and long-time dynamic simulation systems based on geometric graph learning. Additionally, I will provide an outlook on the future of AI for Science-related fields.<\/span><\/section>\n

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      ESTAG\uff1aEquivariant Spatio-Temporal Attentive Graph Networks<\/p>\n<\/li>\n

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      SEGNO: Second-order Equivariant Graph Neural Ordinary Differential Equation<\/p>\n<\/li>\n

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      DEGNN: Discrete Equivariant Graph Neural Network<\/p>\n<\/li>\n<\/ol>\n

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        HGIN: Geometric Graph Learning for Protein Mutation Effect Prediction<\/p>\n<\/li>\n<\/ol>\n

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          \u56fe\u795e\u7ecf\u7f51\u7edc Graph Neural Networks<\/span><\/p>\n

          \u7b49\u53d8\u79bb\u6563\u5085\u91cc\u53f6\u53d8\u6362 Equivariant Discrete Fourier Transform<\/span><\/p>\n

          \u795e\u7ecf\u5e38\u5fae\u5206\u65b9\u7a0b Neural Ordinary Differential Equations<\/span><\/p>\n

          \u86cb\u767d\u7a81\u53d8\u6548\u5e94\u9884\u6d4b Protein Mutation Effect Prediction<\/span><\/p>\n

          \u7269\u7406\u52a8\u6001\u5b66\u4e60 Physical Dynamics Learning<\/span><\/section>\n


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          \u8363\u94b0\u535a\u58eb\uff0cIEEE Senior Member\uff0c\u963f\u91cc\u5df4\u5df4\u8fbe\u6469\u9662\u57fa\u7840\u667a\u80fd\u4e2d\u5fc3\u8d44\u6df1\u6280\u672f\u4e13\u5bb6\uff0c\u8d1f\u8d23AI for Science\u65b9\u5411<\/strong>\u3002\u8363\u94b0\u535a\u58eb\u57282016\u5e74\u4e8e\u9999\u6e2f\u4e2d\u6587\u5927\u5b66\u83b7\u5f97\u7cfb\u7edf\u5de5\u7a0b\u4e0e\u5de5\u7a0b\u7ba1\u7406\u535a\u58eb\u5b66\u4f4d\u53ca\u8fdb\u884c\u535a\u58eb\u540e\u7814\u7a76\u30022017\u5e746\u6708\u52a0\u5165\u817e\u8bafAI Lab\uff0c\u4efb\u804c\u4e13\u5bb6\u7814\u7a76\u5458\uff0c\u8d1f\u8d23\u817e\u8baf\u4e91\u6df1\u667a\u836f\u5e73\u53f0\u5206\u5b50\u5c5e\u6027\u9884\u6d4b\u6a21\u5757\u5efa\u8bbe, \u540c\u65f6\u4f5c\u4e3a\u7ec4\u7ec7\u8005\u53c2\u4e0e\u7b2c\u4e8c\u5c4aOpen Catalyst Challenge,\u83b7\u5f97\u7b2c\u4e00\u540d\u30022024\u5e746\u6708\u52a0\u5165\u8fbe\u6469\u9662\uff0c\u8d1f\u8d23AI for Science\u65b9\u5411\u7814\u7a76\u3002\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u4e3a\u56fe\u6df1\u5ea6\u5b66\u4e60\uff0c\u5927\u89c4\u6a21\u56fe\u7cfb\u7edf\uff0cAI for Science\u7b49\u3002\u8c37\u6b4c\u5b66\u672f\u5f15\u75288000\u4f59\u6b21\uff0c\u5355\u7bc7\u6700\u9ad8\u5f15\u75281400\u4f59\u6b21\u3002H-index 38\u3002\u4e24\u7bc7\u6587\u7ae0\u5165\u9009PaperDigest \u6700\u5177\u5f71\u54cd\u529b\u8bba\u6587\u3002\u5165\u9009\u65af\u5766\u798f\u5927\u5b66\u4e16\u754cTop 2%\u79d1\u5b66\u5bb6\u3002<\/span><\/section>\n

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          \u62db\u8058\u4fe1\u606f\uff1a<\/strong><\/span><\/section>\n
          \u8fbe\u6469\u9662AI for Science\u5b9e\u9a8c\u5ba4\u6821\u62db\u8fdb\u884c\u65f6\uff01\u5927\u91cf\u6821\u62db\u804c\u4f4d\u5f00\u653e\uff0c\u65b9\u5411\u6d89\u53ca\u57fa\u7840\u6a21\u578b\u5f00\u53d1\uff0c\u5e94\u7528\u5f00\u53d1\uff0c\u6a21\u62df\u8ba1\u7b97\uff0c\u8bad\u7ec3\u52a0\u901f\u7b49\u804c\u4f4d\u3002\u8ba9\u6211\u4eec\u4e00\u8d77\u6253\u5f00\u6709\u610f\u601d\u7684\u672a\u6765\uff01<\/span><\/section>\n

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          \u5177\u4f53\u6295\u9012\u5165\u53e3\uff1a<\/strong><\/span><\/section>\n
          https:\/\/talent-holding.alibaba.com\/campus\/position-list?campusType=freshman&lang=zh&search=AI%20for%20Science<\/span><\/section>\n


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          Liu, Y., Cheng, J., Zhao, H., Xu, T., Zhao, P., Tsung, F., … & Rong, Y. SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases. In The Twelfth International Conference on Learning Representations<\/em>.<\/span><\/p>\n

          \u63a8\u8350\u8bed\uff1a\u672c\u6587\u63d0\u51fa\u4e86\u57fa\u4e8e\u79bb\u6563\u7b49\u53d8\u7fa4\u7684\u7b49\u53d8\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u901a\u8fc7\u653e\u677e\u4e25\u683c\u7b49\u53d8\u5047\u8bbe\uff0c\u4f7f\u5f97\u6a21\u578b\u53ef\u4ee5\u66f4\u597d\u7684\u5efa\u6a21\u5b9e\u9645\u7269\u7406\u573a\u666f\uff0c\u4f8b\u5982\u4ea4\u901a\u52a8\u6001\u9884\u6d4b\uff0c\u4eba\u7fa4\u52a8\u6001\u9884\u6d4b\u7b49\u3002<\/span><\/p>\n

          Zheng, Z., Liu, Y., Li, J., Yao, J., & Rong, Y. (2024). Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning. arXiv preprint arXiv:2406.16295<\/em>.<\/span><\/p>\n

          \u63a8\u8350\u8bed\uff1a\u672c\u6587\u901a\u8fc7\u5f15\u5165\u5386\u53f2\u8f68\u8ff9\u6765\u5bf9\u65f6\u7a7a\u6570\u636e\u4e2d\u975e\u9a6c\u5c14\u53ef\u592b\u4ea4\u4e92\u5efa\u6a21\uff0c\u540c\u65f6\u8bbe\u8ba1\u57fa\u4e8e\u7b49\u53d8\u7684\u5085\u91cc\u53f6\u53d8\u6362\u6a21\u5757\u53bb\u5b66\u4e60\u5386\u53f2\u8f68\u8ff9\u4e2d\u5b58\u5728\u7684\u5468\u671f\u89c4\u5f8b\u6765\u5b9e\u73b0\u5bf9\u591a\u5c3a\u5ea6\u52a8\u6001\u5efa\u6a21\u7684\u51c6\u786e\u9884\u6d4b\u3002<\/span><\/p>\n

          Wu, L., Hou, Z., Yuan, J., Rong, Y., & Huang, W. (2024). Equivariant spatio-temporal attentive graph networks to simulate physical dynamics. Advances in Neural Information Processing Systems<\/em>, 36<\/em>.<\/span><\/p>\n

          \u63a8\u8350\u8bed\uff1a\u672c\u6587\u901a\u8fc7\u5f15\u5165\u5c42\u6b21\u5316\u7b49\u53d8\u56fe\u7f51\u7edc\u5b9e\u73b0\u4e86\u5bf9\u86cb\u767d\u8d28\u7a81\u53d8\u6548\u5e94\u7684\u51c6\u786e\u9884\u6d4b\u3002<\/span><\/p>\n

          Zhao, K., Rong, Y., Jiang, B., Tang, J., Zhang, H., Yu, J. X., & Zhao, P. (2023, October). Geometric Graph Learning for Protein Mutation Effect Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management<\/em> (pp. 3412-3422).<\/span><\/p>\n

          \u63a8\u8350\u8bed\uff1a\u672c\u6587\u4ece\u6570\u636e\uff0c\u6a21\u578b\uff0c\u5e94\u7528\u7684\u89d2\u5ea6\u7cfb\u7edf\u68b3\u7406\u4e86\u5f53\u524d\u51e0\u4f55\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u76f8\u5173\u8fdb\u5c55<\/span><\/p>\n

          Han, J., Cen, J., Wu, L., Li, Z., Kong, X., Jiao, R., … & Huang, W. (2024). A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications. arXiv preprint arXiv:2403.00485<\/em>.<\/span><\/section>\n

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