Data-driven multiscale modeling for correcting dynamical systems

Karl Otness

doi:10.1088/2632-2153/ae1a36

2025-12-19

Karl Otness *

Laure Zanna

Joan Bruna

DOI：10.1088/2632-2153/ae1a36

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摘要

En 中文

我们提出了一种多尺度方法用于预测动力系统中的量，该方法明确地结构化以在由细到粗和由粗到细两个方向上提取信息。与具有类似网络架构的非多尺度方法相比，我们的方法在计算量最小增加的情况下提高了模型的准确性和稳定性。我们在一个理想化的流体子网格参数化（称为闭包）任务上评估了我们的方法，在该任务中，我们的多尺度网络校正了混沌的基础模型，以反映未解析、细尺度动力学的影响。

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期刊

machine learning: science and technology

IF:

论文数:

116

被引数:

机构

courant institute school of mathematics, computing, and data science, new york university, new york, ny, united states of america

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论文数: 1

被引数: 0

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