混合数据物理设备数字双胞胎的自我进化使用元学习和持续学习.
IEEE transactions on cybernetics
|January 28, 2026
概括
本研究介绍了一种新的混合方法,用于自进化的设备数字双胞胎 (DTs),确保物理资产的准确实时镜像. 该方法使用数据物理集成与元学习和持续学习进行自适应模型更新.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 数字双胞胎 (DTs) 需要持续更新,以准确反映物理资产的行为.
- 现有的方法难以自主适应动态的现实世界的变化.
- 弥合基于物理模型和实时数据之间的差距是一个挑战.
研究的目的:
- 为了引入一种新的混合方法,用于自我进化的设备数字双胞胎.
- 为了使DTs能够连续和准确地对物理对应器进行镜像.
- 为了提高数字双胞胎的适应性和实时准确性.
主要方法:
- 一种数据物理驱动的方法,整合了元学习和持续学习.
- 使用Koopman自编码器 (KAE) 进行扩展的残余模型.
- 在线基础模型培训中使用爬行动物元学习算法.
- 实施一个由事件触发的机制,用于在线持续学习更新.
主要成果:
- 拟议的方法使数字双胞胎能够使用实时传感器数据自主更新模型.
- 混合方法确保快速适应新的,未见的场景.
- 通过机器人模拟案例研究进行验证,证明了效率和性能的提高.
结论:
- 线下元学习和在线持续学习的融合促进了敏捷的数字双胞胎进化.
- 该框架确保数字双胞胎准确地反映实时物理设备的状态.
- 这种方法显著提高了设备数字双胞胎的自我进化能力.
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