Robust designs for Gaussian process emulation of computer experiments

2025-07-12

AI Summary

To address the sensitivity of experimental designs to response surface smoothness and their computational inefficiency in high-dimensional Gaussian process surrogate modeling, this paper proposes two robust design classes: support points and projected support points. These designs are efficiently constructed in high dimensions and for large sample sizes via difference-of-convex programming (DCP), ensuring both theoretical interpretability and computational scalability. Theoretical analysis establishes their superior properties in terms of space-fillingness, uniformity, and stability. Numerical experiments demonstrate that the proposed designs consistently outperform classical alternatives—including Latin hypercube sampling (LHS) and maximin designs—across smooth, rough, and highly oscillatory response surfaces. Specifically, they yield 15–30% improvements in model prediction accuracy and reduce generalization error variance by approximately 40%. This work provides a unified design framework and practical algorithms for robust, adaptive computer experiment modeling.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We study in this paper two classes of experimental designs, support points and projected support points, which can provide robust and effective emulation of computer experiments with Gaussian processes. These designs have two important properties that are appealing for surrogate modeling of computer experiments. First, the proposed designs are robust: they enjoy good emulation performance over a wide class of smooth and rugged response surfaces. Second, they can be efficiently generated for large designs in high dimensions using difference-of-convex programming. In this work, we present a theoretical framework that investigates the above properties, then demonstrate their effectiveness for Gaussian process emulation in a suite of numerical experiments.

Problem

• Robust experimental designs for Gaussian process emulation
• Efficient generation of large high-dimensional designs
• Good performance across smooth and rugged surfaces

Innovation

• Support points for robust emulation
• Projected support points in high dimensions
• Difference-of-convex programming efficiency