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提高基于深度学习的模拟器在三维湖水力学中的可解释性和通用性,使用库普曼运算符和转移学习:以苏黎世湖为例进行演示

Wenchong Tian¹, Zhiyu Zhang², Damien Bouffard³

¹College of Environmental Science and Engineering, Tongji University, 200092 Shanghai, China; Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 200092 Shanghai, China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, Shanghai 200092, P.R. China.

|December 16, 2023

概括

本研究介绍了一个深度学习模拟器,用于快速的3D湖水力动力学建模. 通过整合Koopman操作员和转移学习,它提高了解释性和通用性,提高了湖泊系统的预测准确性.

科学领域:

环境建模环境建模
计算流体动力学计算流体动力学
机器学习应用程序机器学习应用程序

背景情况:

三维湖水力动力学模型对于评估水文变化至关重要,但也面临着计算方面的挑战.
现有的深度学习 (DL) 模拟器缺乏可解释性和通用性,限制了它们的实际使用.

研究的目的:

开发一个数据驱动的模拟器,用于快速的3D湖水力动力学模拟.
提高基于DL的模拟器的可解释性和通用性,使用库普曼运算符和转移学习 (TL).
分析基于DL的模拟器在湖水力学中的通用性.

主要方法:

使用深度神经网络 (DNN) 创建了一个数据驱动的模拟器,用于3D湖水力动力学.
应用了库普曼运算理论和转移学习 (TL) 来提高模拟器性能.
使用线性回归和相关性分析来评估可概括性.

主要成果:

开发的模拟器 (DLEDMD) 与标准DNN相比,由于库普曼运算符揭示了底层线性结构,因此显示出更高的解释性.
模拟器的概括性对训练和测试数据集之间的相似性敏感.
转移学习显著提高了基于DL的模拟器的通用性.

结论:

库普曼运算符提高了基于DL的水力动力学模型的解释性.
转移学习是一种有效的策略,可以提高DL模拟器对湖水力学的概括性.
这项研究为快速湖水力动力学预测提供了更易于解释和概括的方法.

关键词:

深度学习是一种深度学习.可以概括的概括性可以解释性解释性库普曼运营商运营商三维湖水力动力学模型.转移学习转移学习

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