采样高斯静态随机场:一个随机实现方法
Bin Zhu1, Jiahao Liu1, Zhengshou Lai2
1School of Intelligent Systems Engineering, Sun Yat-sen University, Gongchang Road 66, 518107 Shenzhen, China.
ISA transactions
|August 21, 2023
概括
这项研究引入了一种有效的方法来生成随机现场样本,这对于地质材料建模和不确定性量化至关重要. 这种基于自回归移动平均线 (ARMA) 模型的新方法在计算上比传统方法便宜.
科学领域:
- 地质科学 地质科学
- 计算数学 计算数学 计算数学
- 工程 工程师 工程师 工程师
背景情况:
- 产生静态随机场的大型样本对于地质材料建模和不确定性量化至关重要.
- 传统的协同变量矩阵分解方法在计算上昂贵,特别是在高维随机场上.
研究的目的:
- 为采样高斯静态随机场提出一种高效的随机实现方法.
- 开发一种比传统方法更便宜的计算替代方案,用于生成随机现场样本.
主要方法:
- 使用系统和控制理论开发了一种高效的随机实现方法.
- 构建了理性光谱密度,并通过对指数和平方指数共变函数的光谱因子化获得了自回归移动平均 (ARMA) 模型.
- 集成了多尺度模拟的方法,使样本在更细微的尺度上进行插值.
主要成果:
- 通过使用低阶ARMA模型在空间领域高效地生成高斯静止随机场的样本.
- 与协方差矩阵分解方法相比,证明了计算效率.
- 成功地将该方法应用于具有精确样本插值的多尺度模拟.
结论:
- 拟议的基于ARMA的随机实现方法为采样高斯静止随机场提供了一个计算效率高且有利的替代方案.
- 这种方法显著降低了计算成本,使其适用于像地质材料建模这样的领域的大规模和多规模应用.
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