Problem
Research questions and friction points this paper is trying to address.
Urgent Interactive HPC
high-fidelity 3D nonlinear analysis
seismic estimation
resource harvesting
rapid decision-making
From Idle to Urgent: A Resource-Harvested HPC Workflow for High-Fidelity Seismic Estimation
🤖 AI Summary
提出了一种高效计算工作流，利用闲置计算资源和神经网络加速大地震时的高精度地震估计，大幅减少紧急情况下的计算成本。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Urgent Interactive HPC
Resource Harvesting
Surrogate Neural Networks
High-Fidelity 3D Nonlinear Analysis
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