Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.
翻译:高斯过程代理是直接使用计算昂贵的模拟模型的普遍替代方法。当模拟输出包含许多响应时,通常会使用降维技术来构建这些代理。但是,具有降维的代理方法通常依赖于完整的输出训练数据。
本文提出了一种新的高斯过程代理方法,可以允许使用部分观测到的输出,并保持计算效率。新方法涉及缺失值的插补和用于高斯过程推断的协方差矩阵的调整。生成的代理代表可用的响应,忽略缺失的响应,并提供有意义的不确定性量化。在模拟研究和一个能量密度泛函模型进行校准的案例研究中,发现所提出的方法比其他方法具有更准确的推断能力。