新架构PINN可精准预测多类复杂多孔介质流动传热问题
A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium
- 采用分支-主干网络分离捕捉全局与局部特征
- 在多孔介质中成功解决正/逆问题及迁移学习任务
- 相比传统方法更擅长大规模反问题求解，适合工程应用
本文提出一种新型基于物理信息神经网络（PINN）的分支-主干（TB）网络架构，通过全连接网络作为主干网，为不同输出配置独立分支网，并利用自动微分计算输入输出的偏导数，结合多种物理损失项。该方法旨在解决多孔介质中的四类核心难题：流动正问题、传热正问题、传热反问题及迁移学习问题，这些难题传统PINN难以应对。实验验证了该架构在多个正问题上的有效性，迁移学习测试证明其资源复用可行性。结合在反问题求解上对传统数值方法的优越性，该方法展现出显著的工程应用潜力。
原文摘要 · Abstract (English)
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.
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