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, ...
The experimental design was based on a Latin hypercube sampling approach, considering different levels of welding speed, wire feed speed, and welding voltage. Specifically, six levels were set for wire feed speed, four for welding voltage, and four for welding speed. From this initial experimental...
Fig. 3. Scatter plots of 620 points used for training a machine learning model, sampled via Latin Hypercube Sampling. 2.3. Machine learning model Deep learning (DL) provides a robust tool for predicting heat storage and extraction in PBLHS. This is another advantage of the current study as ...
(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,...
If people want to quickly obtain the solution of the engineering scheme, perhaps some simple heuristic algorithms such as Latin hypercube sampling could be more recommended. But we cannot guarantee the result validity of these algorithms. Fortunately, we do not need to worry about the validity ...
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Then the Kriging metamodel is set up with 100 initial test points constructed by the Latin hypercube sampling method, and 10,000 sets of Conclusions In this paper, the major contribution is to characterize the nonlinear responses and perform reliability analysis by replacing the finite element ...
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....
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...
(ωi)+1whereMis the harmonic order andωiis a characteristic frequency assigned to thei-th random variable. If the uncertainty analysis request several input parameters, the periodic sampling frequenciesωiwill be large. As a result, the model can become computationally prohibitive. For these ...