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On the estimation of the curvatures and bending rigidity of membrane networks via a local maximum-entropy approach

机译：用局部最大熵方法估计膜网络的曲率和弯曲刚度

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摘要

We present a meshfree method for the curvature estimation of membrane networks based on the local maximum entropy approach recently presented in [1]. A continuum regularization of the network is carried out by balancing the maximization of the information entropy corresponding to the nodal data, with the minimization of the total width of the shape functions. The accuracy and convergence properties of the given curvature prediction procedure are assessed through numerical applications to benchmark problems, which include coarse grained molecular dynamics simulations of the fluctuations of red blood cell membranes [2,3]. We also provide an energetic discrete-to-continuum approach to the prediction of the zero-temperature bending rigidity of membrane networks, which is based on the integration of the local curvature estimates. The local maximum entropy approach is easily applicable to the continuum regularization of fluctuating membranes, and the prediction of membrane and bending elasticities of molecular dynamics models.
机译：我们基于[1]中提出的局部最大熵方法，提出了一种无网格的膜网络曲率估计方法。通过平衡与节点数据相对应的信息熵的最大化与形状函数的总宽度的最小化，可以实现网络的连续正则化。给定曲率预测程序的准确性和收敛性通过数值应用来评估基准问题，包括对红细胞膜波动的粗粒分子动力学模拟[2,3]。我们还基于局部曲率估计值的集成，提供了一种高能离散到连续谱方法来预测膜网络的零温弯曲刚度。局部最大熵方法很容易应用于波动膜的连续正则化，以及分子动力学模型的膜和弯曲弹性的预测。

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