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<p>Using <i>in-situ</i> 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 (<i>r</i>) and root mean square error (RMSE) between the estimated and observed lg(<i>ε</i>) (where <i>ε</i> denotes 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 <i>r</i> and RMSE by 35% and 75%, respectively.</p>

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Enhancing the generalization of turbulent mixing parameterization by physics-informed machine learning

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Minghao Hu,
  • Lingling Xie,
  • Mingming Li,
  • Xiaotong Chen

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 lg(ε) (where ε denotes 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.