Latin Hypercube SamplingMonte Carlo integrationPeriodic spaceϕ criterionAudze-Egl a ¯ jsMaximin criterion,In this paper, the family of p optimization criteria for space-filling designs is critically reviewed, with a focus on its behavior in moderate to large dimensions, especially for small ...
Optimal Experiment Design, Latin HypercubeLatin hypercube sampling Latin hypercube design is a way to generate design points that can spread observations evenly over the range of each input variable. For a Latin hypercube design of size n,...doi:10.1007/978-1-4419-9863-7_1233Ying Hung...
Based on the derived constrained zone, an optimization system comprised of the Computational Fluid Dynamics (CFD) analysis, the Latin Hypercube Sampling (LHS) method, the approximate model, and the Multi-Objective Particle Swarm Optimization (MOPSO) was established. Taking the 1:2.5 scaled 1400-MW...
Grid sampling From the perspective of PDE, we need to establish a discrete domain to price American put option using the combined PINN. Commonly used grid generation methods include uniform grid, equispaced grid, Latin hypercube sampling, and so on. Pricing analysis of American options by generatin...
Multivariate sensitivity analyses based on Latin Hypercube Sampling are performed to investi- gate the effects of input parameters on the control pol- icy structure and the mean cumulative deaths. Additional studies investigate the effects of departures from the modeling assumptions, which include expo-...
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 ...
To describe this uncertain environment, the Latin hypercube sampling with Cholesky decomposition simulation method is used to sample uncer- tain wind speeds. An improved optimization algorithm, group search optimizer with intraspecific competition and le´vy walk, is then used to optimize the MV ...
(Citation2014) were the first to use PSO for the construction of optimal designs and generated Latin-hypercube designs. Chen et al. (Citation2014) and Mak and Joseph (Citation2018) applied PSO to generate space-filling designs. PSO for generating optimal designs for non-linear models were ...
Latin Hypercube sampling is performed to generate input data for rigorous Aspen Plus simulations. Piecewise linear surrogate models are derived by solving GDP problems based on high-fidelity data from Aspen Plus and nonlinear capital cost correlations. In the following step, a hybrid MILP master ...
The divergence metrics were computed for replicated (n = 10) sample plans using the conditioned Latin hypercube sampling algorithm across increasing samples sizes of 10, 25, and 50 to 400 in steps of 50 to determine an optimal sample size; the sensitivity of the divergence metrics to increasing...