Bayesian Quadrature Optimization for Probability Threshold Robustness Measure
This addresses a practical challenge in product development for engineers, but it is incremental as it applies existing methods like Gaussian Processes to a specific robustness measure.
The paper tackles the problem of optimizing product design parameters to maximize the probability of meeting performance requirements under environmental variations, by formulating it as active learning problems and proposing algorithms with theoretical guarantees, achieving efficient results in synthetic and real-world scenarios.
In many product development problems, the performance of the product is governed by two types of parameters called design parameter and environmental parameter. While the former is fully controllable, the latter varies depending on the environment in which the product is used. The challenge of such a problem is to find the design parameter that maximizes the probability that the performance of the product will meet the desired requisite level given the variation of the environmental parameter. In this paper, we formulate this practical problem as active learning (AL) problems and propose efficient algorithms with theoretically guaranteed performance. Our basic idea is to use Gaussian Process (GP) model as the surrogate model of the product development process, and then to formulate our AL problems as Bayesian Quadrature Optimization problems for probabilistic threshold robustness (PTR) measure. We derive credible intervals for the PTR measure and propose AL algorithms for the optimization and level set estimation of the PTR measure. We clarify the theoretical properties of the proposed algorithms and demonstrate their efficiency in both synthetic and real-world product development problems.