LHS is a stratified random sampling method originally developed for efficient uncertainty assessment. LHS partitions the parameter space into bins of equal probability with the goal of attaining a more even distribution of sample points in the parameter space that would be possible with pure random sampling.
The pysmo.sampling.LatinHypercubeSampling method carries out Latin Hypercube sampling. This can be done in two modes:
- The samples can be selected from a user-provided dataset, or
- The samples can be generated from a set of provided bounds.
idaes.surrogate.pysmo.sampling.LatinHypercubeSampling
[1] Loeven et al paper titled "A Probabilistic Radial Basis Function Approach for Uncertainty Quantification" https://pdfs.semanticscholar.org/48a0/d3797e482e37f73e077893594e01e1c667a2.pdf
[2] Webpage on low discrepancy sampling methods: http://planning.cs.uiuc.edu/node210.html
[3] Swiler, Laura and Slepoy, Raisa and Giunta, Anthony: "Evaluation of sampling methods in constructing response surface approximations" https://arc.aiaa.org/doi/abs/10.2514/6.2006-1827