A Non-stationary Thermal-Block Benchmark Model for Parametric Model Order Reduction

非平稳信号的参数建模

Parametric Modeling of Non-Stationary Signals

本文提出基于信号特征的非平稳信号参数建模方法，用于准确估计模型参数，应用于瞬态系统响应、语音语素和心电图信号等。

An adaptive block Bregman proximal gradient method for computing stationary states of multicomponent phase-field crystal model

In this paper, we compute the stationary states of the multicomponent phase-field crystal model by formulating it as a block constrained minimization problem. The original infinite-dimensional non-convex minimization problem is approximated by a finite-dimensional constrained non-convex minimization problem after an appropriate spatial discretization. To efficiently solve the above optimization problem, we propose a so-called adaptive block Bregman proximal gradient (AB-BPG) algorithm that fully exploits the problem's block structure. The proposed method updates each order parameter alternativ…

用于块正则网格上平稳高斯随机场模拟的块循环嵌入方法

A Block Circulant Embedding Method for Simulation of Stationary Gaussian Random Fields on Block-regular Grids

本文提出一种新的方法，用于在非正则但具有正则块结构的网格上采样平稳高斯随机场，该方法在典型模型问题中优于传统循环嵌入方法。

GTH算法、截断马尔可夫链和块形式$RG$-因子分解

GTH Algorithm, Censored Markov Chains, and $RG$-Factorization in Block-Form

本文研究了块形式GTH算法与截断马尔可夫链的关系，提出了一种改进的截断转移矩阵，提升了无限状态马尔可夫链的近似精度。

核矩阵的参数化分层矩阵逼近

Parametric Hierarchical Matrix Approximations to Kernel Matrices

本文提出参数化分层矩阵新类，通过离线多项式逼近与张量压缩、在线高效实例化，避免重复核函数求值，实现对核矩阵超参数相关计算超过100倍加速。

Weighted block compressed sensing for parametrized function approximation

In this paper we extend results taken from compressed sensing to recover Hilbert-space valued vectors. This is an important problem in parametric function approximation in particular when the number of parameters is high. By expanding our target functions in a polynomial chaos and assuming some compressibility of such an expansion, we can exploit structured sparsity (typically a group sparsity structure) to recover the sequence of coefficients with high accuracy. While traditional compressed sensing would typically expect a number of snapshots scaling exponentially with the number of parameter…