文献类型：期刊文献

中文题名：Enhancing the generalization of turbulent mixing parameterization by physics-informed machine learning

作者：Minghao Hu[1];Lingling Xie[1,2,3];Mingming Li[1,2,3];Xiaotong Chen[1]

机构：[1]Laboratory of Coastal Ocean Variability and Disaster Prediction,College of Ocean and Meteorology,Guangdong Ocean University,Zhanjiang 524088,China;[2]Key Laboratory of Climate,Resources and Environments in Continent Shelf Sea and Deep Ocean,Zhanjiang 524088,China;[3]Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Waters,Zhanjiang 524088,China

年份：2025

卷号：44

期号：12

起止页码：79

中文期刊名：Acta Oceanologica Sinica

外文期刊名：海洋学报(英文版)

基金：The National Science and Technology Major Project under contract No.2024YFC2817003;the National Natural Science Foundation of China under contract Nos 42276019 and 42249911;the Guangdong Ordinary University Innovation Team Project under contract No.2023KCXTD015.

语种：英文

中文关键词：microstructure observations;turbulent mixing;physics-informed machine learning;generalization

中文摘要：Using in-situ microstructure observations from 2010 to 2018,this study investigates the performance and generalization of machine learning models in parameterizing turbulent mixing in the northwestern South China Sea.The results show that the data-driven extreme gradient boosting(XGBoost)performs better than the other four models,i.e.,random forest,neural network,linear regression and support vector machine regression.In order to further improve the generalization of machine learning-based parameterization method,we propose a physics-informed machine learning(PIML)that couples the MacKinnon-Gregg model(known as the MG model)and Osborn’s formula to the XGBoost model.The correlation coefficient(r)and root mean square error(RMSE)between the estimated and observed 1g(ε)(whereεdenotes the turbulent kinetic energy dissipation rate)from the PIML are improved by 14%and 16%,respectively.The results also show that PIML effectively improves the generalization of the XGBoost-based parameterization method,enhancing r and RMSE by 35%and 75%,respectively.