Efficient stochastic FEM for flow in heterogeneous porous media. Part 1: random Gaussian conductivity coefficients

This paper is concerned with the development of efficient iterative methods for solving the linear system of equations arising from stochastic FEMs for single‐phase fluid flow in porous media. It is assumed that the conductivity coefficient varies randomly in space according to some given correlation function and is approximated using a truncated Karhunen–Loève expansion. Distinct discretizations of the deterministic and stochastic spaces are required for implementations of the stochastic FEM. In this paper, the deterministic space is discretized using classical finite elements and the stochastic space using a polynomial chaos expansion. The highly structured linear systems which result from this discretization mean that Krylov subspace iterative solvers are extremely effective. The performance of a range of preconditioned iterative methods is investigated and evaluated in terms of robustness with respect to mesh size and variability of the conductivity coefficient. An efficient symmetric block Gauss–Seidel preconditioner is proposed for problems in which the conductivity coefficient has a large standard deviation.The companion paper, herein, referred to as Part 2, considers the situation in which Gaussian random fields are transformed into lognormal ones by projecting the truncated Karhunen–Loève expansion onto a polynomial chaos basis. This results in a stochastic nonlinear problem because the random fields are represented using polynomial chaos containing terms that are generally nonlinear in the random variables. Copyright © 2013 John Wiley & Sons, Ltd.     

由扩散张量导出的各向异性扩散模型的隐式数值模拟

研究了由扩散张量导出的各向异性扩散的图像处理模型,并构造了隐式差分格式,形成了有13条对角线的大型稀疏矩阵.利用代数多重网格法求解了这个线性代数方程组.并进行了数值试验.     

求解二维三温能量方程的基于AMG预条件子的Krylov子空间迭代法

本文对一类二维三温能量方程的实际应用问题，建立了一种半粗化的代数多重网格法（SAMG），进而得到了以该SAMG方法为预条件子的Krylov子空间迭代法。数值实验结果表明，该方法对求解二维三温能量方程的实际问题是十分有效和健壮的。     

Improving solve time of aggregation‐based adaptive AMG

This paper proposes improving the solve time of a bootstrap algebraic multigrid (AMG) designed previously by the authors. This is achieved by incorporating the information, a set of algebraically smooth vectors, generated by the bootstrap algorithm, in a single hierarchy by using sufficiently large aggregates, and these aggregates are compositions of aggregates already built throughout the bootstrap algorithm. The modified AMG method has good convergence properties and shows significant reduction in both memory and solve time. These savings with respect to the original bootstrap AMG are illustrated on some difficult (for standard AMG) linear systems arising from discretization of scalar and vector function elliptic partial differential equations in both 2D and 3D.     

α-Mangostin Nanoparticles Cytotoxicity and Cell Death Modalities in Breast Cancer Cell Lines

α-Mangostin (AMG) is a potent anticancer xanthone that was discovered in mangosteen (Garcinia mangostana Linn.). AMG possesses the highest opportunity for chemopreventive and chemotherapeutic therapy. AMG inhibits every step in the process of carcinogenesis. AMG suppressed multiple breast cancer (BC) cell proliferation and apoptosis by decreasing the creation of cancerous compounds. Accumulating BC abnormalities and their associated molecular signaling pathways promotes novel treatment strategies. Chemotherapy is a commonly used treatment; due to the possibility of unpleasant side effects and multidrug resistance, there has been substantial progress in searching for alternative solutions, including the use of plant-derived natural chemicals. Due to the limitations of conventional cancer therapy, nanotechnology provides hope for effective and efficient cancer diagnosis and treatment. Nanotechnology enables the delivery of nanoparticles and increased solubility of drugs and drug targeting, resulting in increased cytotoxicity and cell death during BC treatment. This review summarizes the progress and development of AMG’s cytotoxicity and the mechanism of death BC cells. The combination of natural medicine and nanotechnology into a synergistic capital will provide various benefits. This information will aid in the development of AMG nanoparticle preparations and may open up new avenues for discovering an effective BC treatment.     

一种新的并行代数多重网格粗化算法

近年来,受实际应用领域中大规模科学计算问题的驱动,在大规模并行机上实现代数多重网格（AMG）算法成为数值计算领域的研究热点。本文针对经典AMG方法,提出一种新的并行网格粗化算法一多阶段并行RS算法（MPRS）。我们将新算法集成到了高性能预条件子软件包Hypre中。大量数值实验结果显示,新算法适合更广泛的问题,相对其他并行粗化算法,明显地改善了AMG并行计算的可扩展性。对三维27点格式有限差分离散的Poisson方程,在64个处理机上并行AMG求解,含8百万个未知量,新算法比RS3算法减少了近60的三维Poisson方程,近32万个未知量,在16个处理机上并行AMG—GMRES求解,新算法所需的迭代步数大约为其他粗化算法的一半,显示了很好的算法可扩展性。     

并行代数多重网格算法可扩展性能分析

对当今求解大型稀疏线性代数方程组最有效的迭代方法之--代数多重网格(AMG)算法的并行计算进行可扩展性能分析.给出一套并行计算可扩展性能分析方法,用于分析和指导并行迭代算法及实现技术的设计与优化并应用于并行AMG算法.分析表明,网格算子的平均模式大小和迭代过程的算法效率分别制约了AMG算法启动阶段和迭代求解阶段并行性能的发挥,成为该类算法急需解决的两个关键问题.     

An algebraic multigrid-based algorithm for circuit clustering

Digital circuits have grown exponentially in their sizes over the past decades. To be able to automate the design of these circuits, efficient algorithms are needed. One of the challenging stages of circuit design is the physical design where the physical locations of the components of a circuit are determined. Coarsening or clustering algorithms have become popular with physical designers due to their ability to reduce circuit sizes in the intermediate design steps such that the design can be performed faster and with higher quality. In this paper, a new clustering algorithm based on the algebraic multigrid (AMG) technique is presented. In the proposed algorithm, AMG is used to assign weights to connections between cells of a circuit and find cells that are best suited to become the initial cells for clusters, seed cells. The seed cells and the weights between them and the other cells are then used to cluster the cells of a circuit. The analysis of the proposed algorithm proves linear-time complexity, O(N), where N is the number of pins in a circuit. The numerical experiments demonstrate that AMG-based clustering can achieve high quality clusters and improve circuit placement designs with low computational cost.     

Solution of linear systems from an optimal control problem arising in wind simulation

Several solution strategies for a class of large, sparse linear systems with a block 2 × 2 structure arising from the finite element discretization of an optimal control problem in wind simulation are introduced and analyzed. Block preconditioners and a sparse direct solver on the original coupled system are compared with a preconditioned GMRES iteration applied to a reduced system (Schur complement). Theoretical and experimental results demonstrate the effectiveness of the reduced system approach. Copyright © 2009 John Wiley & Sons, Ltd.