Batch-sequential design and heteroskedastic surrogate modeling for delta smelt conservation

作者：Boya Zhang, Robert B. Gramacy, Leah R. Johnson, Kenneth A. Rose, Eric Smith · 发表于：The Annals of Applied Statistics · 年份：2022 · DOI：10.1214/21-aoas1521 · 被引用次数：10 · 研究领域：Gaussian Processes and Bayesian Inference、Advanced Multi-Objective Optimization Algorithms、Optimal Experimental Design Methods

Delta smelt is an endangered fish species in the San Francisco estuary that have shown an overall population decline over the past 30 years. Researchers have developed a stochastic, agent-based simulator to virtualize the system with the goal of understanding the relative contribution of natural and anthropogenic factors that might play a role in their decline. However, the input configuration space is high dimensional, running the simulator is time-consuming, and its noisy outputs change nonlinearly in both mean and variance. Getting enough runs to effectively learn input–output dynamics requires both a nimble modeling strategy and parallel evaluation. Recent advances in heteroskedastic Gaussian process (HetGP) surrogate modeling helps, but little is known about how to appropriately plan experiments for highly distributed simulation. We propose a batch sequential design scheme, generalizing one-at-a-time variance-based active learning for HetGP, as a means of keeping multicore cluster nodes fully engaged with runs. Our acquisition strategy is carefully engineered to favor selection of replicates which boost statistical and computational efficiency when training surrogates to isolate signal from noise. Design and modeling are illustrated on a range of toy examples before embarking on a large-scale smelt simulation campaign and downstream high-fidelity input sensitivity analysis.