Fast numerical methods for robust optimal design

A fast numerical approach for robust design optimization is presented. The core of the method is based on the state-of-the-art fast numerical methods for stochastic computations with parametric uncertainty. These methods employ generalized polynomial chaos (gPC) as a high-order representation for random quantities and a stochastic Galerkin (SG) or stochastic collocation (SC) approach to transform the original stochastic governing equations to a set of deterministic equations. The gPC-based SG and SC algorithms are able to produce highly accurate stochastic solutions with (much) reduced computational cost. It is demonstrated that they can serve as efficient forward problem solvers in robust design problems. Possible alternative definitions for robustness are also discussed. Traditional robust optimization seeks to minimize the variance (or standard deviation) of the response function while optimizing its mean. It can be shown that although variance can be used as a measure of uncertainty, it is a weak measure and may not fully reflect the output variability. Subsequently a strong measure in terms of the sensitivity derivatives of the response function is proposed as an alternative robust optimization definition. Numerical examples are provided to demonstrate the efficiency of the gPC-based algorithms, in both the traditional weak measure and the newly proposed strong measure.     

仿真系统中DAE求解技术现状

目的 通过对仿真系统中微分代数方程（Differential Algebraic Equations,DAE）求解过程的描述,协助开发人员在仿真建模求解方面有更加深入的理解,便于分析和查找仿真建模中的问题。方法 利用指标约简技术将DAE方程的求解问题转化为ODE方程的求解问题,并利用数值求解和误差估计等现有技术进行求解。结果 通过对现有技术进行分析和归纳概括,总结了DAE系统现有技术在指标约简、数值求解和误差分析等关键环节中存在的优点和不足。结论 指出了仿真建模领域未来需要重点研究的方向。     

常微分方程特征值问题求解器解法的改进

对于工程中的常微分方程(ODE)特征值问题,已有一套完整的算法,并据此算法开发出ODE特征值求解程序COLEGN。该程序需要用户输入直接求解(非线性)和逆幂迭代(线性)两套ODE体系,输入繁琐,不便于使用。针对这一问题,研究了这两套ODE体系间的内部联系,建立了从非线性ODE体系获取线性ODE体系的具体途径,并据此改写了COLEGN程序,简化了用户的输入,使之更易于使用,另外,还对边界条件含特征值的情况作了相应处理,拓宽了算法的适用范围。     

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.     

任意阶次多项式最小二乘拟合不确定度计算方法与最佳拟合阶次分析

阐述了一种采用矩阵方程表示的任意阶次多项式最小二乘拟合的不确定度计算方法,同时通过仿真研究了该方法的特性。通过该方法,可以得到拟合不确定度最小的拟合阶次,也就是最佳的拟合阶次。仿真得到的最佳拟合阶次与仿真模型的原函数阶次都为5阶,从而验证了该方法的有效性。     

非平稳随机激励下高维非线性系统可靠度分析的概率密度全局演化方法

实际工程结构遭受的灾害性动力作用(如强风、地震等)往往具有显著的随机性和非平稳性.对复杂随机激励下高维非线性系统的动力可靠度进行精细化分析,对于实际工程结构的抗灾设计和优化具有重要意义.基于一般连续随机过程的降维概率密度演化方程,给出了一类非平稳随机激励下的高维非线性系统动力可靠度分析方法.具体地,若仅针对系统某一感兴趣物理量在给定安全域下的首次超越问题,则可以构造该物理量在安全域内的吸收边界过程,并建立其瞬时概率密度函数满足的二维偏微分方程,即降维概率密度演化方程.方程中的本征漂移系数是驱动概率密度演化的全局性物理驱动力,可以通过对原系统有限次代表性确定性动力分析获取的数据进行数值构造.采用数值方法求解降维概率密度演化方程,即可获得系统的动力可靠度解答.文中通过两个算例验证了该方法的有效性,并讨论了需要进一步研究的问题.     

随机激励下结构振动及其控制系统差分方程的统一模式

本文研究了结构振动及其控制系统差分方程统一模式的建立问题。对状态变量的观测,仅测量结构的前几个位移或速度。将随机输入和观测噪声等维化处理后,建立了结构振动及其控制系统差分方程的统一模式。