Latin hypercube sampling (LHS) is one technique that stands out in this context. LHS selects representative samples through permutations in a multidimensional space, effectively preventing the clustering of points in specific areas. However, its limitations become apparent in high-dimensional problems, ...
Latin Hypercube Sampling Techniques for Power Systems Reliability Analysis With Renewable Energy Sources. This paper proposes Latin hypercube sampling (LHS) methods for reliability analysis of power systems including renewable energy sources, with an emphasis o... Shu,Zhen,Jirutitijaroen,... - 《IEEE...
(2003). On Latin hypercube sampling for structural reliability analysis. Structural Safety, 25, 47–68. CrossRef Helton, J. C., & Davis, F. J. (2003). Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering and System Safety, 81,...
The Latin hypercube sampling (LHS) methodology is then employed to generate a quasi-random sampling distribution (S) within the given parameter limits, resulting in a set of configuration vectors (𝐕configVconfig). For each configuration vector (𝑣𝑖→vi→) in the set, the wing, tail, ...
The SA has been carried out considering more than 100 samples and mostly Simple Random Sampling (SRS), but also Latin Hypercube Sampling (LHS) methods were tested. Table 9. Core cases: parameters and distributions used for the SA. At the core level, several figures of merit (reactivity, ...
To obtain additional knowledge about the design space a Latin-hypercube sampling could be employed. This would likely lead to higher accuracy in the statistical model, but would also increase the required computation time, as the number of experiments would most likely increase. The use of process...
from 12 h upwards. The lowest 6 h time limit deviates slightly from the FMC results at around 2.1 × 106m2. Nevertheless, the overall performance of Kriging for Inverurie is significantly improved in comparison to current uncertainty quantification methods (FMC, Latin Hypercube Sampling, and ML...
6.1.1. Latin Hypercube Sampling Distribution The performance error and regression coefficient between the predicted and actual output data are shown in Figure 16 for the case of data distributed according to the LHS algorithm. Each bar corresponds to one size of the hidden layer of the ANN (i....
1Citations Metricsdetails Abstract A non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale flow problems. The objective is to perform accurate and rapid uncer...
Audze–Eglais and Maximum Entropy Sampling (MES) [190]. Another approach maximizes Euclidean distance between all points in the DoE [141]. Among the modern DoE methods, [77], one of the most commonly used is the Latin Hypercube Sampling (LHS) [135], distributing a fixed number of samples...