论文标题
物理知识的深度学习，用于不可压缩的层流流
Physics-informed deep learning for incompressible laminar flows
论文作者
论文摘要
物理知识的深度学习在近年来引起了极大的兴趣解决计算物理问题，其基本概念是嵌入物理定律以约束/告知神经网络，需要更少的数据来培训可靠的模型。这可以通过将物理方程的残差纳入损失函数来实现。通过最小化损耗函数，网络可以近似解决方案。在本文中，我们提出了一种用于流体动力学的物理信息神经网络（PINN）的混合变量方案，并将其应用于模拟较低的雷诺数下的稳定和瞬时层流流。参数研究表明，混合变量方案可以提高PINN的训练性和解决方案的准确性。还将提出的PINN方法的预测速度和压力场与参考数值溶液进行了比较。模拟结果证明了拟议的PINN对流体流量模拟的巨大潜力，其精度很高。
Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inform neural networks, with the need of less data for training a reliable model. This can be achieved by incorporating the residual of physics equations into the loss function. Through minimizing the loss function, the network could approximate the solution. In this paper, we propose a mixed-variable scheme of physics-informed neural network (PINN) for fluid dynamics and apply it to simulate steady and transient laminar flows at low Reynolds numbers. A parametric study indicates that the mixed-variable scheme can improve the PINN trainability and the solution accuracy. The predicted velocity and pressure fields by the proposed PINN approach are also compared with the reference numerical solutions. Simulation results demonstrate great potential of the proposed PINN for fluid flow simulation with a high accuracy.
