Multi-objective Bayesian alloy design using multi-task Gaussian processes
Multi-objective Bayesian alloy design using multi-task Gaussian processes
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DOI：
10.1016/j.matlet.2023.135067
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发表时间：
2023-11
影响因子：
3
通讯作者：
Danial Khatamsaz;Brent Vela;R. Arróyave
中科院分区：
文献类型：
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作者：
Danial Khatamsaz;Brent Vela;R. Arróyave
在设计应用中，材料性能之间的相关性（例如，更强的材料的韧性较低的趋势）往往被忽视。这种方法在多目标优化技术中得到了回应，该技术将每个性能特性视为独立目标，旨在优化标量函数并找到最佳Pareto前沿。然而，这忽略了材料系统中固有的性能特性之间的统计关系。为了解决这个问题，我们建议使用贝叶斯优化，一个高效的黑箱优化算法，已知的构造高斯过程（GP）-不相关的代理-模型的目标函数。而不是单独评估每个目标函数的多个GP，我们主张转向联合建模这些目标函数，考虑到它们的统计相关性。这种集成方法利用材料特性之间自然发生的关系，提供额外的信息，以提高设计框架的性能。这需要用单个多任务GP替换多个独立GP，采用相关矩阵来构建多任务核函数，其中每个任务对应于单个目标函数。我们预计这种改进的方法将更好地利用材料相关性，改善设计优化结果。
In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. We anticipate this refined methodology will better leverage material correlations, improving design optimization results.