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This paper presents a physics-informed neural network (PINN) framework for modeling transient elastodynamic wave propagation in bimaterial systems, using a steel-aluminum specimen from a Split Hopkinson Pressure Bar. The PINN accurately predicts wave transmission and reflection, validated against high-fidelity finite-element simulations, and serves as a continuous surrogate model for elastodynamic analysis.