Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization. (arXiv:2108.08890v2 [stat.ML] UPDATED)

Web: http://arxiv.org/abs/2108.08890

Optimizing the reliability and the robustness of a design is important but
often unaffordable due to high sample requirements. Surrogate models based on
statistical and machine learning methods are used to increase the sample
efficiency. However, for higher dimensional or multi-modal systems, surrogate
models may also require a large amount of samples to achieve good results. We
propose a sequential sampling strategy for the surrogate based solution of
multi-objective reliability based robust design optimization problems. Proposed
local Latin hypercube refinement …

arxiv design ml optimization uncertainty