论文标题
tt-pinn：用于边缘计算的张量压缩神经PDE求解器
TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing
论文作者
论文摘要
物理知识的神经网络（PINN）由于对复杂物理系统进行建模的能力而越来越多地使用。为了获得更好的表现力，在许多问题中需要越来越大的网络大小。当我们需要培训有限的内存，计算和能源资源的边缘设备上的Pinns时，这引起了挑战。为了在边缘设备上进行训练PINN，本文提出了基于张量 - 培训分解的端到端压缩PINN。在求解Helmholtz方程时，我们提出的模型显着优于原始PINN，几乎没有参数，并且可以实现令人满意的预测，最多15美元$ \ times $ $总体参数还原。
Physics-informed neural networks (PINNs) have been increasingly employed due to their capability of modeling complex physics systems. To achieve better expressiveness, increasingly larger network sizes are required in many problems. This has caused challenges when we need to train PINNs on edge devices with limited memory, computing and energy resources. To enable training PINNs on edge devices, this paper proposes an end-to-end compressed PINN based on Tensor-Train decomposition. In solving a Helmholtz equation, our proposed model significantly outperforms the original PINNs with few parameters and achieves satisfactory prediction with up to 15$\times$ overall parameter reduction.
