标题： 一种用于多孔介质中流动和传热预测的新型主干分支网络PINN 显示英文标题

标题： A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium

摘要： 一种新颖的Trunk-Branch（TB）-net物理信息神经网络（PINN）架构被开发出来，这是一种基于PINN的方法，结合了Trunk网络和Branch网络以捕捉全局和局部特征。 目标是解决四类主要问题：正向流动问题、正向传热问题、逆向传热问题以及多孔介质内的迁移学习问题，这些问题非常复杂，无法通过原始PINN处理。 在提出的TB-net PINN架构中，使用全连接神经网络（FNN）作为Trunk网络，随后根据输出分别使用独立的FNN作为Branch网络，并通过考虑各种物理损失来对输出相对于输入的偏导数进行自动微分。 通过一系列正向问题展示了新型TB-net PINN架构的有效性和灵活性，迁移学习验证了资源复用的可行性。 结合在求解逆向问题上对传统数值方法的优势，所提出的TB-net PINN显示出其在实际工程应用中的巨大潜力。 显示英文摘要

摘要： A novel Trunk-Branch (TB)-net physics-informed neural network (PINN) architecture is developed, which is a PINN-based method incorporating trunk and branch nets to capture both global and local features. The aim is to solve four main classes of problems: forward flow problem, forward heat transfer problem, inverse heat transfer problem, and transfer learning problem within the porous medium, which are notoriously complex that could not be handled by origin PINN. In the proposed TB-net PINN architecture, a Fully-connected Neural Network (FNN) is used as the trunk net, followed by separated FNNs as the branch nets with respect to outputs, and automatic differentiation is performed for partial derivatives of outputs with respect to inputs by considering various physical loss. The effectiveness and flexibility of the novel TB-net PINN architecture is demonstrated through a collection of forward problems, and transfer learning validates the feasibility of resource reuse. Combining with the superiority over traditional numerical methods in solving inverse problems, the proposed TB-net PINN shows its great potential for practical engineering applications.