Abstract Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironically make them less tractable and transparent. This paper introduces an active learning methodology for adaptively constructing surrogate models, i.e. emulators, of such costly computer codes in a multi-output setting. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. It aims to achieve accurate approximations, model tractability, as well as compact and expressive simulated datasets. In order to achieve this, the proposed Active Multi-Output Gaussian Process Emulator (AMOGAPE) combines the predictive capacity of Gaussian Processes (GPs) with the design of an acquisition function that favors sampling in low density and fluctuating regions of the approximation functions. Comparing different acquisition functions, we illustrate the promising performance of the method for the construction of emulators with toy examples, as well as for a widely used remote sensing transfer code.

中文翻译：

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用高斯过程主动仿真计算机代码——在遥感中的应用

摘要 许多科学和工程领域依赖于使用复杂且计算成本高的模型运行模拟来理解感兴趣系统中涉及的过程。然而，涉及的高成本阻碍了可靠和详尽的模拟。通常，此类代码包含启发式方法，具有讽刺意味的是使它们更不易处理和透明。本文介绍了一种主动学习方法，用于在多输出设置中自适应地构建这种昂贵的计算机代码的替代模型，即仿真器。所提出的技术是连续的和自适应的，并且基于合适的采集函数的优化。它旨在实现准确的近似值、模型的可处理性以及紧凑且富有表现力的模拟数据集。为了实现这一目标，提议的有源多输出高斯过程仿真器 (AMOGAPE) 将高斯过程 (GP) 的预测能力与有利于在近似函数的低密度和波动区域采样的采集函数的设计相结合。比较不同的采集函数，我们用玩具例子说明了构建模拟器的方法的有前途的性能，以及广泛使用的遥感传输代码。