通过神经操作员的积极学习来发现和预测极端事件
Ethan Pickering1, Stephen Guth2, George Em Karniadakis3
1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. pickering@mit.edu.
Nature computational science
|January 4, 2024
概括
这项研究引入了一种人工智能驱动的框架,用于表征罕见的极端事件. 通过将贝叶斯实验设计与深度神经运算符相结合,它可以有效地在多种系统中识别关键情况.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 复杂系统分析 复杂系统分析
背景情况:
- 大自然和社会中的极端事件带来了重大风险,但由于它们的稀有性和潜在系统的复杂性,它们的特征具有挑战性.
- 传统方法在从有限或不完整的数据中识别和量化罕见事件方面存在固有的困难.
研究的目的:
- 开发一种新的,可扩展的人工智能 (AI) 辅助框架,以有效地描述和推断极端事件.
- 克服现有方法在复杂,潜在的无限维度系统内分析罕见现象的局限性.
主要方法:
- 利用了一个模型不可知框架,将贝叶斯实验设计 (BED) 的输出加权训练与深度神经运算符组合相结合.
- 采用BED计划进行主动数据选择以量化极端事件和深度神经运算符以近似非线性运算符.
主要成果:
- 与高斯过程相比,拟议的人工智能框架表现出优越的性能.
- 关键发现包括使用浅层组合的最佳性能,无论初始数据如何,有效检测极端事件,消除"双下降"现象,以及使用低于最佳采样的强大性能.
- 蒙特卡洛收购在高维设置中被证明比标准优化器更有效.
结论:
- 开发的AI基础设施提供了一个可扩展的解决方案,可以有效地推断和确定各种领域的关键情况.
- 这种方法通过利用先进的人工智能技术来增强理解和预测灾难性事件的能力.
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