立即体验 拉丁超立方采样(Latin Hypercube Sampling, LHS)是一种统计采样技术,用于生成一组样本点,这些样本点在参数空间中均匀分布,并且满足每个维度只有一个样本点落在该维度的取值范围内。LHS常用于参数优化和模型验证等领域。在Python中,有几种常用的包可以进行拉丁超立方采样,下面介绍两种常用的方法。1. 使用pyDOE...
Latin Hypercube Sampling.. 拉丁超立方体抽样 --- (1979))在 “Technometrics” 提出了“拉丁超立方体抽样” (Latin Hypercube Sampling) (简称LHS) 的方法，并立即得到广泛的应用，一批学者对其理论和方法作了系统地研究和发展，形成了一个独立的分枝。
探索未知的科学殿堂，拉丁超立方采样（Latin Hypercube Sampling, LHS）与蒙特卡洛模拟如同夜空中的璀璨星辰，照亮了处理不确定性问题的迷雾。蒙特卡洛，这个名字本身就蕴含着一种随机的魔力，它通过海量的模拟实验，为我们揭示概率世界的奥秘，哪怕是看似简单的抛硬币游戏，也能借此估算出五次投掷中出现一次正...
1.Both Simple Random Sampling andLatin Hypercube Samplingwere applied to simulate the model of the soil retardation factor of the herbicide mefenacet by Monte Carlo method.采用简单随机抽样和拉丁超立方抽样两种不同方法对除草剂苯噻草胺的土壤滞留因子模型进行蒙特卡洛模拟 。 2.Three methods for designing t...
This technique, called constrained Latin hypercube sampling (cLHS), consists in doing permutations on an initial LHS to honor the desired monotonic constraints. The relevance of this approach is shown on a real example concerning the numerical welding simulation, where the inequality constraints are ...
拉丁超立方采样是一种特殊的采样方法，用于改进蒙特卡洛模拟中的随机抽样。它通过将样本空间分层，然后在每层内随机抽样，最后将样本打乱顺序，得到结果。相比于随机抽样，拉丁超立方采样在复杂分布场景下的效率更高，能够更均匀地覆盖样本空间，尤其是在正态分布等复杂分布的情况下。拉丁超立方采样的主要优点...
Latin hypercube sampling (LHS) uses a stratified sampling scheme to improve on the coverage of the k-dimensional input space for such computer models. This means that a single sample will provide useful information when some input variable(s) dominate certain responses (or certain time intervals)...
Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods global-optimizationgaussian-processeslatin-hypercube-samplingsurrogate-modelsmixture-of-experts UpdatedNov 30, 2024 Rust Provides a number of methods for creating and augmenting Latin Hypercube...
以下是基于最大最小思想优化的拉丁超立方采样(LHS)的改写代码: import numpy as np def latin_hypercube_sampling(n, d): samples = np.zeros((n, d))
Latin hypercube sampling (LHS) and updated Latin hypercube sampling (ULHS) by statistical correlation reducing equation are employed to analyze structural reliability sensitivity and its variance. Numerical and engineering examples with single failure mode and with multiple failure modes are used to demonst...