Towards the Evaluation of the Relevant Degrees of Freedom in Nonlinear Partial Differential Equations

We investigate an operator renormalization group method to extract and describe the relevant degrees of freedom in the evolution of partial differential equations. The proposed renormalization group approach is formulated as an analytical method providing the fundamental concepts of a numerical algorithm applicable to various dynamical systems. We examine dynamical scaling characteristics in the short-time and the long-time evolution regime providing only a reduced number of degrees of freedom to the evolution process.     

Probabilistic approach to homoclinic chaos

Three-dimensional systems possessing a homoclinic orbit associated to a saddle focus with eigenvalues ±i, – and giving rise to homoclinic chaos when the Shil'nikov condition < is satisfied are studied. The 2D Poincaré map and its 1D contractions capturing the essential features of the flow are given. At homoclinicity, these 1D maps are found to be piecewise linear. This property allows one to reduce the Frobenius—Perron equation to a master equation whose solution is analytically known. The probabilistic properties such as the time autocorrelation function of the state variablex are explicitly derived.     

高分子粗粒化分子动力学模拟进展

模拟与理论、实验相互补充,彼此验证,使我们在原子和分子尺度上理解相关实验现象中的微观物理机制。结合高性能计算机和计算技术的发展,模拟已发展成为独特的高分子科学研究手段,在高分子材料优化设计和性能与结构问的性能关系研究中起重要作用。高分子具有不同层次的结构,材料的性质和功能不仅取决于化学结构和分子性质,而且很大程度上取决...     

On the connection between Zwanzig's classical projection operator formalism and coarse-graining

It is pointed out that the following two procedures are identical: (i) applying Zwanzig's projection operator to the distribution function; (ii) coarsegraining the distribution function with cells which are defined by means of the macroscopic variables and their inaccuracies, and going to the limit of vanishing inaccuracies.     

Construction of Coarse-Grained Models by Reproducing Equilibrium Probability Density Function

The present work proposes a novel methodology for constructing coarse-grained(CG) models, which aims at minimizing the difference between CG model and the corresponding original system. The difference is defined as a functional of their equilibrium conformational probability densities, then is estimated from equilibrium averages of many independent physical quantities denoted as basis functions. An orthonormalization strategy is adopted to get the independent basis functions from sufficiently preselected interesting physical quantities of the system. Thus the current method is named as probability density matching coarse-graining(PMCG) scheme, which effectively takes into account the overall characteristics of the original systems to construct CG model, and it is a natural improvement of the usual CG scheme wherein some physical quantities are intuitively chosen without considering their correlations. We verify the general PMCG framework in constructing a one-site CG water model from TIP3 P model. Both structure of liquids and pressure of the TIP3 P water system are found to be well reproduced at the same time in the constructed CG model.     

基于复杂度分析logistic映射和Lorenz模型的研究

采用三次粗粒化方法得到了logistic映射和Lorenz模型的符号序列，运用动态非线性时间序 列分析方法——Lemper-Ziv复杂度，分别对两组符号序列进行了对比分析.对于logistic映 射，其复杂度反映了时间序列的演化；Lorenz模型三个分量的复杂度序列都具有混沌性质， 即由许多振幅非常接近而长度完全不同的循环所组成，反映了Lorenz模型内在的准周期特性 .进一步研究发现，当取不同的窗口长度时，复杂度序列的特征基本相同，并且复杂度反映 了时间序列的时空特性.因此，可以借助复杂度的计算来反演观测资料的动力学结构. 关键词： 三次粗粒化 Lemper-Ziv复杂度 logistic映射 Lorenz模型     

A Family of Fitness Landscapes Modeled through Gene Regulatory Networks

Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and “ruggedness”. As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population’s evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies.     

Covariant fuzzy observables and coarse-graining

A fuzzy observable is regarded as a smearing of a sharp observable, and the structure of covariant fuzzy observables is studied. It is shown that the covariant coarse-grainings of sharp observables are exactly the covariant fuzzy observables. A necessary and sufficient condition for a covariant fuzzy observable to be informationally equivalent to the corresponding sharp observable is given.