We propose an Urgent Interactive HPC workflow that dynamically integrates high-fidelity 3D nonlinear analysis with surrogate neural networks (NNs) to enable rapid decision-making during large-scale earthquakes. This approach achieves both "Resource Harvesting", which utilizes idle computing capacity during non-emergency periods, and immediate response during crises. By developing two specialized HPC kernels, the proposed method reduces energy-to-solution by 76% and improves throughput by 3.7-fold during normal operations to efficiently construct training datasets, while during emergencies, it couples NN-based inverse analysis with physics-based simulations and dynamic refinement to reduce conventional computational costs by over 97.9%, enabling the generation of highly reliable spatial time-history ground motion distributions within 30 minutes post-earthquake.

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