基于AutoML的工作流程的实验设计 (DOE) 选择和基准测试数据采集策略与模拟模型
Xukuan Xu1, Donghui Li2, Jinghou Bi3
1Aschaffenburg University of Applied Sciences, Faculty of Engineering, Aschaffenburg, 63743, Germany. xukuan.xu@th-ab.de.
Scientific reports
|December 31, 2024
概括
积极学习 (AL) 采样策略可以优化实验设计 (DOE) 资源分配. 然而,并非所有AL策略都能超过传统DOE,这取决于数据量,复杂性和不确定性. 对杂数据来说,复制策略仍然很有价值.
科学领域:
- 实验设计 实验设计
- 机器学习 机器学习
背景情况:
- 实验设计 (DOE) 对于高效的参数空间探索至关重要.
- 基于模型的积极学习 (AL) 提供了优化数据采样策略的潜力.
研究的目的:
- 引入使用自动机器学习进行DOE比较研究的工作流程.
- 检查系统数据生成和不确定性下的模型性能之间的相互作用.
主要方法:
- 开发了一个工作流程,集成DOE和自动机器学习.
- 定义的模型复杂性实际上适用于机器学习环境.
- 从采样,数据精度和建模中调查的不确定性.
主要成果:
- 并非所有AL策略都优于传统DOE;性能取决于数据量,复杂性和不确定性.
- 以复制为导向的策略对于不可忽视的噪音和中间资源是有利的.
- 系统地分析了数据复制和广泛采样之间的权衡.
结论:
- 拟议的工作流模拟了DOE实际的测试和选择条件.
- AL战略的有效性取决于背景,并不普遍优于DOE.
- 在ML驱动的DOE中,平衡数据复制和探索是资源分配的关键.
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