In this study, we have explored an application of deep learning architecture of the U-Net model, originally designed for biomedical image segmentation, in a regression analysis aimed at predicting fluid flows through textured microchannels. The data for this analysis is generated using the lattice Boltzmann method through extensive simulations, capturing the intricate behaviors of fluid dynamics in a microscale environment. The raw simulation data was meticulously preprocessed to prepare it for training the U-Net model, ensuring that the input features and labels were appropriately formatted and normalized to optimize the learning process of the model. The U-Net model, with its inherent capability of capturing spatial hierarchies and producing better predictions, proved effective in this novel application. We have evaluated the performance of the model using metrics including MSE, RMSE, MAE, and $R^2$ scores. These metrics were crucial in assessing the accuracy and reliability of the model predictions. The results demonstrate that the U-Net model can predict fluid flows with high accuracy and less error, indicating its potential for broader applications in fluid dynamics and other fields requiring precise regression modeling. A parametric analysis of the U-Net with attention mechanism showed that the velocity field prediction is contingent upon the solid-fluid interaction parameter and surface wettability. The U-Net equipped with an attention mechanism predicts the velocity magnitude and components for textured microchannels with an average error of 5.18%, which upon optimization may subsequently lower to 2.1%. The U-Net model including an attention mechanism (U-Net AM) regularly surpasses the conventional U-Net model in all measures, evidencing enhanced accuracy and generalization.

翻译：本研究探索了最初为生物医学图像分割设计的U-Net模型深度学习架构在回归分析中的应用，旨在预测纹理微通道中的流体流动。分析数据通过格子玻尔兹曼方法进行大量模拟生成，捕捉了微尺度环境中流体动力学的复杂行为。原始模拟数据经过精心预处理，为训练U-Net模型做好准备，确保输入特征和标签得到适当格式化和归一化，以优化模型的学习过程。U-Net模型凭借其捕捉空间层次结构和产生更优预测的固有能力，在这一新颖应用中证明了其有效性。我们使用MSE、RMSE、MAE和$R^2$分数等指标评估了模型性能。这些指标对于评估模型预测的准确性和可靠性至关重要。结果表明，U-Net模型能够以高精度和低误差预测流体流动，表明其在流体动力学及其他需要精确回归建模的领域具有更广泛的应用潜力。对具有注意力机制的U-Net的参数分析表明，速度场预测取决于固液相互作用参数和表面润湿性。配备注意力机制的U-Net预测纹理微通道速度大小和分量的平均误差为5.18%，经优化后可能降至2.1%。包含注意力机制的U-Net模型（U-Net AM）在所有指标上均稳定超越传统U-Net模型，证明了其更高的准确性和泛化能力。