Estimating physical plate parameters from impulse responses is a challenging inverse problem. Task A of the first Digital Audio Effects Parameter Estimation Challenge requires the recovery of six identifiable parameters from displacement impulse responses. In this work, we propose a physics-inspired feature fusion network (PIFFN) that combines a pretrained convolutional backbone with a 15-dimensional physics-inspired feature vector computed from the impulse response. These physics-inspired features describe amplitude scale, temporal decay, and spectral structure without relying on modal-distribution priors. The proposed model is evaluated on the official validation set, achieving an overall normalized mean squared error of 0.00362. Compared with the official particle swarm optimization baseline and backbone-only model, PIFFN shows a clear performance improvement, demonstrating its effectiveness for plate parameter estimation.