Multi-fidelity cost-aware Bayesian optimization
Multi-fidelity cost-aware Bayesian optimization
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DOI：
10.1016/j.cma.2023.115937
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发表时间：
2023-02-20
影响因子：
7.2
通讯作者：
Bostanabad，Ramin
中科院分区：
文献类型：
--
作者：
Foumani，Zahra Zanjani;Shishehbor，Mehdi;Bostanabad，Ramin
贝叶斯优化（BO）越来越多地用于材料设计和药物发现等关键应用。BO中越来越流行的策略是放弃对高保真数据的唯一依赖，而是使用提供廉价低保真数据的信息源的集合。该策略的总体前提是通过查询其数据与高保真样本相关的廉价低保真源来降低总采样成本。在这里，我们提出了一个多保真度的成本感知BO框架，大大优于国家的最先进的技术，在效率，一致性和鲁棒性。我们展示了我们的框架在分析和工程问题上的优势，并认为这些好处源于我们的两个主要贡献：（1）我们为多保真度成本感知BO开发了一种新的采集功能，以保护收敛免受低保真度数据的偏见，以及（2）我们为多保真度BO定制了一个新开发的仿真器，使我们不仅能够同时从多保真度数据集的集合中学习，而且还可以识别应该从BO中排除的严重偏见的低保真度源。
Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble of information sources which provide inexpensive low-fidelity data. The overall premise of this strategy is to reduce the total sampling costs by querying inexpensive low-fidelity sources whose data are correlated with high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO framework that dramatically outperforms the state-of-the-art technologies in terms of efficiency, consistency, and robustness. We demonstrate the advantages of our framework on analytic and engineering problems and argue that these benefits stem from our two main contributions:(1) we develop a novel acquisition function for multi-fidelity cost-aware BO that safeguards the convergence against the biases of low-fidelity data, and (2) we tailor a newly developed emulator for multi-fidelity BO which enables us to not only simultaneously learn from an ensemble of multi-fidelity datasets, but also identify the severely biased low-fidelity sources that should be excluded from BO.