Quasi-random sampling technique for efficient space exploration
Latin Hypercube Sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. It divides each dimension into equal intervals and ensures exactly one sample per interval, achieving better space-filling properties than pure random sampling with fewer samples. This makes it particularly useful for computer experiments, sensitivity analysis, and Monte Carlo simulations.