  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.
