🤖 AI Summary
This work addresses the sampling bias in traditional Maximum Projection (MaxPro) designs caused by the use of Euclidean distance over bounded domains, which leads to under-sampling near boundaries—particularly corners—and introduces bias in Monte Carlo estimates. To remedy this, the authors propose the uniform Maximum Projection (uMaxPro) method, which replaces Euclidean distance with a periodic distance based on the minimum image convention. This modification preserves the Latin hypercube structure while eliminating boundary-induced density distortions, thereby achieving both uniformity in low-dimensional subspaces and overall statistical uniformity. The uMaxPro design significantly reduces bias and variance in Monte Carlo integration, enhances subspace projection performance and discrepancy, and improves the accuracy of surrogate models and the reliability of probabilistic estimates in engineering benchmark problems such as mesoscale finite element modeling of concrete.
📝 Abstract
Space-filling experimental designs are widely used in engineering computer experiments, where only a limited number of expensive model evaluations can be afforded. Distance-based designs such as Maximin or Minimax ensure global space-filling, while Latin hypercube sampling enforces uniform one-dimensional projections, yet neither guarantees uniformity in lowdimensional subspaces. Maximum Projection (MaxPro) designs were introduced to improve uniformity in low-dimensional subspaces, yet their original formulation relies on the Euclidean distance and may induce systematic density distortions in bounded domains. We demonstrate that the standard MaxPro criterion leads to statistically non-uniform sampling, resulting in undersampling of corner regions and biased Monte Carlo estimates.
To remedy this issue, we introduce a periodic variant of the criterion, termed Uniform Maximum Projection (uMaxPro), in which the Euclidean metric is replaced by a periodic distance based on the minimum image convention. The proposed uMaxPro designs preserve the projection-aware structure of MaxPro while achieving statistical uniformity of the design-generation mechanism. Numerical experiments show unbiased Monte Carlo integration with reduced variance, excellent subspace projection performance, and competitive discrepancy properties. The methodology is further validated on benchmark engineering problems, including a meso-scale finite element model of concrete, demonstrating improved accuracy in surrogate modeling and probabilistic estimation.
The resulting criterion provides a simple and computationally efficient modification of MaxPro that enhances its robustness for nonadaptive computer experiments. The construction algorithm, open-source implementation, and reproducible optimized designs are provided to facilitate practical adoption of the method.