Enhance manuscript content by refining design of experiments section and adding…

Manuscript/wileyNJD-AMA.bib

@@ -557,4 +557,35 @@ steel from coupon test results available. First, the theory of metal plasticity
   urldate   = {2026-05-05},
 }
 
+@Article{Joseph2008,
+  author    = {Joseph, V. Roshan and Hung, Ying},
+  journal   = {Statistica Sinica},
+  title     = {Orthogonal-{Maximin} {Latin} {Hypercube} {Designs}},
+  year      = {2008},
+  issn      = {1017-0405},
+  number    = {1},
+  pages     = {171--186},
+  volume    = {18},
+  abstract  = {A randomly generated Latin hypercube design (LHD) can be quite structured: the variables may be highly correlated or the design may not have good space-filling properties. There are procedures for finding good LHDs by minimizing the pairwise correlations or by maximizing the inter-site distances. In this article we show that these two criteria need not be in close agreement. We propose a multi-objective optimization approach to find good LHDs by combining correlation and distance performance measures. We also propose a new exchange algorithm for efficiently generating such designs. Several examples are presented to show that the new algorithm is fast, and that the optimal designs are good in terms of both the correlation and distance criteria.},
+  publisher = {Institute of Statistical Science, Academia Sinica},
+  urldate   = {2026-05-06},
+}
+
+@Article{Sheikholeslami2017,
+  author     = {Sheikholeslami, Razi and Razavi, Saman},
+  journal    = {Environmental Modelling \& Software},
+  title      = {Progressive {Latin} {Hypercube} {Sampling}: {An} efficient approach for robust sampling-based analysis of environmental models},
+  year       = {2017},
+  issn       = {1364-8152},
+  month      = jul,
+  pages      = {109--126},
+  volume     = {93},
+  abstract   = {Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses.},
+  doi        = {10.1016/j.envsoft.2017.03.010},
+  keywords   = {Design of computer experiments, Monte Carlo simulation, Optimal Latin hypercube sampling, Sensitivity analysis, Sequential sampling, Uncertainty analysis},
+  shorttitle = {Progressive {Latin} {Hypercube} {Sampling}},
+  url        = {https://www.sciencedirect.com/science/article/pii/S1364815216305096},
+  urldate    = {2026-05-06},
+}
+
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