Plate reverberation is characterized by a dense distribution of resonant modes, which makes the estimation of modal parameters from observed responses a challenging inverse problem. To address this problem, we propose a physics guided dual stream framework that integrates an Audio Spectrogram Transformer (AST) with Dynamic Mode Decomposition (DMD). The AST branch models the global temporal and spectral structure of the impulse response, whereas the DMD branch extracts local descriptors associated with modal dynamics. The resulting representations are fused and processed by convolutional prediction heads to jointly estimate mode presence and the corresponding modal parameters. Experiments on the official validation set of Task B in the DAFx Challenge show that the proposed method reduces the overall relative error from 1.976 for the official baseline to 0.867. These results demonstrate that integrating local dynamic information derived from physical modeling with global transformer based representations substantially improves plate modal parameter estimation.