一个概率的图形模型基础,使得数字双胞胎能够在规模上进行预测
Michael G Kapteyn1, Jacob V R Pretorius2, Karen E Willcox3
1Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nature computational science
|January 13, 2024
概括
本研究介绍了一个统一的数学框架,使用可扩展数字双胞胎的概率图形模型. 这种方法可以为各种应用,包括无人驾驶飞行器,实现可靠,原则性校准和更新.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数学 数学 是一个数学.
背景情况:
- 数字双胞胎需要超越自定义实现的可扩展,统一的数学公式.
- 目前的方法缺乏一个强大的框架,用于大规模的数字双胞胎部署.
研究的目的:
- 提出一个概率图形模型作为数字双胞胎的正式数学表示.
- 为了实现可扩展,强大和原则性的数字双胞胎实现.
主要方法:
- 将资产双胞胎系统抽象化为合动态系统.
- 用概率的图形模型来进行正式的数学定义.
- 利用贝叶斯统计学,动态系统和控制理论.
主要成果:
- 为无人机 (UAV) 展示了结构数字双胞胎的实例化.
- 通过实验UAV数据成功校准了数字双胞胎.
- 插图动态更新和决策与飞行中的损害模拟.
结论:
- 概率图形模型为数字双胞胎校准和更新提供了统一和可扩展的基础.
- 拟议的模型是严格的,灵活的,适用于各种领域.
- 这个框架支持整个数字双胞胎车队的管理.
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