论文标题
转移学习元模型，使用应用于自然对流流的人工神经网络
A transfer learning metamodel using artificial neural networks applied to natural convection flows in enclosures
论文作者
论文摘要
在本文中，我们采用了转移学习技术来预测围栏中自然对流流的努塞尔特数。具体而言，我们考虑了一个二维正方形围墙的基准问题，该围栏具有孤立的水平壁和垂直壁，在恒定温度下。瑞利和prandtl数字是足够的参数来数值模拟此问题。我们采用了两种解决这个问题的方法：首先，我们利用多网格数据集来以具有成本效益的方式培训我们的人工神经网络。通过监视该数据集的训练损失，我们检测到了源于网格大小不足的任何明显异常，我们通过更改网格大小或添加更多数据来进一步纠正。其次，我们试图通过使用深层神经网络进行转移学习来赋予我们的元模型，以便能够考虑其他输入功能。我们训练了具有单个输入功能（Rayleigh）的神经网络，并将其扩展为结合第二个功能（PRANDTL）的效果。我们还考虑了空心外壳的情况，表明我们的学习框架可以应用于具有更高身体复杂性的系统，同时降低了计算和培训成本。
In this paper, we employed a transfer learning technique to predict the Nusselt number for natural convection flows in enclosures. Specifically, we considered the benchmark problem of a two-dimensional square enclosure with isolated horizontal walls and vertical walls at constant temperatures. The Rayleigh and Prandtl numbers are sufficient parameters to simulate this problem numerically. We adopted two approaches to this problem: Firstly, we made use of a multi-grid dataset in order to train our artificial neural network in a cost-effective manner. By monitoring the training losses for this dataset, we detected any significant anomalies that stemmed from an insufficient grid size, which we further corrected by altering the grid size or adding more data. Secondly, we sought to endow our metamodel with the ability to account for additional input features by performing transfer learning using deep neural networks. We trained a neural network with a single input feature (Rayleigh) and extended it to incorporate the effects of a second feature (Prandtl). We also considered the case of hollow enclosures, demonstrating that our learning framework can be applied to systems with higher physical complexity, while bringing the computational and training costs down.
