材料科学优化基准数据集用于硬球包装模拟的多目标,多真实性优化
Sterling G Baird1, Ramsey Issa1, Taylor D Sparks1,2
1Materials Science & Engineering, 122 S. Central Campus Drive, #304 Salt Lake City, UT 84112-0056, United States.
Data in brief
|September 1, 2023
概括
开发有效的科学基准需要具有较低计算开销的现实的模拟. 这项研究创建了硬球包装的替代模型,模仿现实世界的复杂性并提高优化任务的相关性.
科学领域:
- 计算科学是一种计算科学.
- 材料科学是一种材料科学.
- 优化优化 优化优化
背景情况:
- 有效的科学基准对进步至关重要,但往往缺乏与现实世界的任务相似性或具有高的计算成本.
- 由于难度或相关性不足,以及缺乏可访问性,现有的基准可能会阻碍进展.
- 创建无法与地面真相观测区分的替代模型是一个关键目标.
研究的目的:
- 为化学和材料科学优化任务开发一个基准数据集,以密切模仿现实世界的复杂性.
- 创建一个替代模型,准确地表示复杂的模拟数据,包括噪声和故障区域.
- 为了确保基准具有低的可访问性和可重复性计算开销.
主要方法:
- 执行了494,498个硬球包装模拟 (206个CPU天),具有9个输入参数,线性约束和两个离散保真度.
- 将模拟数据记录到MongoDB Atlas数据库中,生成失败概率和回归数据集.
- 开发了一种使用百分位数的替代模型来解释异构排列噪声,避免了传统的先验假设.
主要成果:
- 生成了两个核心表格数据集:失败概率和回归,将输入参数映射到结果.
- 替代模型成功地结合了模拟失败和异种类噪声,非常接近实际的模拟结果.
- 百分位排名方法提供了可靠和准确的数据,与传统的噪音建模方法不同.
结论:
- 开发的替代模型和数据处理技术有效地弥合了低开支基准和复杂的现实世界优化场景之间的差距.
- 这种方法提高了化学和材料科学等领域的科学基准的相关性和可访问性.
- 该方法可扩展到其他基准数据集,促进更现实的,更高效的科学发现.
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