多目标优化,以多任务和多真实性的方法来实现翻动的水力动力学
Zhangyuan Wang1, Dehan Yuan1, Chenglong Wu2
1Zhejiang University, Hangzhou 310027, China.
Physical review. E
|February 17, 2024
概括
我们开发了一种多任务和多忠实度高斯过程 (MMGP) 模型,以优化折叠的性能. 这种方法有效地使用各种数据保真度来降低成本并增强多目标预测.
科学领域:
- 流体动力学 流体动力学
- 机器学习是机器学习.
- 计算物理学的计算物理.
背景情况:
- 折叠是复杂的系统,具有多目标性能特征.
- 为了优化这些系统,采集高保真度数据往往是昂贵且耗时的.
- 现有的模型可能很难有效地整合不同忠实度级别的数据.
研究的目的:
- 开发一种新的多任务和多忠实度高斯过程 (MMGP) 模型.
- 准确地预测和优化翻动的多目标性能.
- 为了最大限度地降低与高保真数据相关的计算成本.
主要方法:
- 在比较了三个内核选项后,使用了光谱混合内核.
- 实施了基于线性先前公式的多忠实性框架,以结合不同的数据忠实性.
- 采用贝叶斯优化与多重获取函数用于多任务主动学习.
主要成果:
- MMGP模型表现出强大而有效的性能.
- 多重获取功能被证明是有效的积极学习.
- 实现了不同忠实度级别的数据的成功集成.
结论:
- 该MMGP模型是一个有能力和高效的框架,用于解决在折叠的优化多目标挑战.
- 这种方法为复杂的流体动力学问题提供了具有成本效益的解决方案.
- 该研究强调了多任务和多忠实性学习在工程应用中的潜力.
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