一个基于高斯过程动态模型的减少顺序模型,用于依赖时间的参数化部分微分方程
Tiantian Wang1, Zhen Gao1,2, Longjiang Mu3
1School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China.
Chaos (Woodbury, N.Y.)
|February 3, 2026
概括
一个新的减少顺序建模框架集成了张量列车分解 (TTD),高斯过程回归 (GPR) 和高斯过程动态模型 (GPDM) 复杂的参数化部分微分方程.
科学领域:
- 计算流体动力学 计算流体动力学
- 数字分析 数字分析
- 机器学习是机器学习.
背景情况:
- 参数化的部分微分方程 (PDEs) 提出了重要的高维挑战.
- 减少顺序建模 (ROM) 对于高效的复杂系统模拟至关重要.
- 现有的ROM与非线性时间动态和不确定性量化作斗争.
研究的目的:
- 为高维度参数化PDEs开发一种新的减少顺序建模框架.
- 整合张力列车分解 (TTD),高斯过程回归 (GPR) 和高斯过程动态模型 (GPDM).
- 为了使精确的时间演变预测和不确定性量化复杂的动态.
主要方法:
- 张力列车分解 (TTD) 用于低级近似的溶液快照.
- 用高斯过程回归 (GPR) 将参数空间映射到TTD格式.
- 高斯过程动态模型 (GPDMs) 用于时间动态建模和不确定性量化.
主要成果:
- 拟议的框架有效地处理高维参数化的PDEs.
- 与传统方法相比,在模拟非线性时间动态方面表现出卓越的准确性.
- 实现了精确的时间域插值和强大的不确定性量化.
结论:
- 综合的TTD-GPR-GPDM框架为复杂的参数化PDEs提供了一个强大的方法.
- 这种方法显著提高了动态系统的减少顺序建模能力.
- 该框架为科学计算中的预测和不确定性评估提供了可靠的工具.
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