标题： 基于核的扩展动态模式分解的运动学一致粗粒化 显示英文标题

标题： Kinetically Consistent Coarse Graining using Kernel-based Extended Dynamic Mode Decomposition

摘要： 在本文中，我们展示了如何使用基于核的Koopman生成器模型——gEDMD方法——来识别在简化变量上的粗粒度动力学，这些变量保留了原始动力学中最慢的转变时间尺度。 本研究的核心是一种学习方法，用于识别粗粒度空间中的有效扩散，这种方法在精神上类似于力匹配方法。 通过利用Koopman生成器的gEDMD模型，可以评估CG模型的动力学准确性。 通过将此方法与适用于有效自由能的适当学习方法相结合，例如力匹配，可以推断出有效动力学的完整模型。 使用二维模型系统和丙氨酸二肽及Chignolin小蛋白的分子动力学模拟数据，我们证明了所提出的方法能够成功且稳健地恢复全模型的基本动力学和热力学特性。 该方法的参数可以使用标准的模型验证技术来确定。 显示英文摘要

摘要： In this paper, we show how kernel-based models for the Koopman generator -- the gEDMD method -- can be used to identify coarse-grained dynamics on reduced variables, which retain the slowest transition timescales of the original dynamics. The centerpiece of this study is a learning method to identify an effective diffusion in coarse-grained space, which is similar in spirit to the force matching method. By leveraging the gEDMD model for the Koopman generator, the kinetic accuracy of the CG model can be evaluated. By combining this method with a suitable learning method for the effective free energy, such as force matching, a complete model for the effective dynamics can be inferred. Using a two-dimensional model system and molecular dynamics simulation data of alanine dipeptide and the Chignolin mini-protein, we demonstrate that the proposed method successfully and robustly recovers the essential kinetic and also thermodynamic properties of the full model. The parameters of the method can be determined using standard model validation techniques.