论文标题
物理学告知神经网络，以解决二维不可压缩的热对流问题
Physics Informed Neural Networks for Two Dimensional Incompressible Thermal Convection Problems
论文作者
论文摘要
物理学知情的神经网络（PINN）近年来引起了工程问题的关注，因为它们的有效性和解决问题的能力而无需产生复杂的网格。 Pinns使用自动分化来评估保护法中的差异操作员，因此不需要具有离散化计划。使用这种能力，PINN在没有任何培训数据的情况下满足了损失功能中物理法律的治疗法则。在这项工作中，我们解决了各种不可压缩的热对流问题，包括实际应用以及具有可比数值或分析结果的问题。我们首先使用分析解决方案来考虑一个通道问题，以显示我们网络的准确性。然后，我们在封闭的外壳中解决了热对流问题，在该封闭的围栏中，流量仅是由于边界上的温度梯度引起的。最后，我们考虑稳定且不稳定的部分阻塞的通道问题，这些问题类似于电力电子产品。
Physics informed neural networks (PINNs) have drawn attention in recent years in engineering problems due to their effectiveness and ability to tackle the problems without generating complex meshes. PINNs use automatic differentiation to evaluate differential operators in conservation laws and hence do not need to have a discretization scheme. Using this ability, PINNs satisfy governing laws of physics in the loss function without any training data. In this work, we solve various incompressible thermal convection problems including real applications and problems with comparable numerical or analytical results. We first consider a channel problem with an analytical solution to show the accuracy of our network. Then, we solve a thermal convection problem in a closed enclosure in which the flow is only due to the temperature gradients on the boundaries. Lastly, we consider steady and unsteady partially blocked channel problems that resemble industrial applications to power electronics.
