用自监督学习捕捉复杂系统演化规律，无需标签即可分析蛋白折叠与气候数据。
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
- 仅用编码器架构，通过自监督学习提取系统演化规律。
- 在蛋白折叠、药物结合和气候数据中成功发现关键动态模式。
- 适合处理海量科学数据的无监督建模，尤其适用于生物与气候研究。
我们提出一种仅含编码器的自监督方法，用于学习大规模非线性动力系统的演化算子，如描述复杂自然现象的系统。演化算子特别适用于分析具有复杂时空模式的系统，已成为多个科学领域的核心分析工具。随着千兆字节级气象数据集和每日可运行数百万分子动力学步数的模拟工具日益普及，该方法为从数据驱动角度理解这些海量信息提供了有效手段。其核心在于自监督表征学习方法与演化算子学习理论之间的深刻关联。为验证方法有效性，我们在多个科学领域进行测试：解释小蛋白的折叠动力学、药物分子在宿主位点的结合过程，以及在气候数据中自主发现模式。实验代码与数据已开源。
原文摘要 · Abstract (English)
We introduce an encoder-only approach to learn the evolution operators of large-scale non-linear dynamical systems, such as those describing complex natural phenomena. Evolution operators are particularly well-suited for analyzing systems that exhibit complex spatio-temporal patterns and have become a key analytical tool across various scientific communities. As terabyte-scale weather datasets and simulation tools capable of running millions of molecular dynamics steps per day are becoming commodities, our approach provides an effective tool to make sense of them from a data-driven perspective. The core of it lies in a remarkable connection between self-supervised representation learning methods and the recently established learning theory of evolution operators. To show the usefulness of the proposed method, we test it across multiple scientific domains: explaining the folding dynamics of small proteins, the binding process of drug-like molecules in host sites, and autonomously finding patterns in climate data. Code and data to reproduce the experiments are made available open source.
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