Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
This enables rapid uncertainty-aware hazard assessment for distributed infrastructure like power grids, though it is domain-specific.
The paper tackles the challenge of generating large ensembles of realistic ground-motion time histories for earthquake hazard analysis, which is computationally intensive with physics-based simulations. GMFlow achieves a 10,000-fold speedup, generating spatially coherent ground motion across over 9 million grid points in seconds.
Earthquake hazard analysis and design of spatially distributed infrastructure, such as power grids and energy pipeline networks, require scenario-specific ground-motion time histories with realistic frequency content and spatiotemporal coherence. However, producing the large ensembles needed for uncertainty quantification with physics-based simulations is computationally intensive and impractical for engineering workflows. To address this challenge, we introduce Ground-Motion Flow (GMFlow), a physics-inspired latent operator flow matching framework that generates realistic, large-scale regional ground-motion time-histories conditioned on physical parameters. Validated on simulated earthquake scenarios in the San Francisco Bay Area, GMFlow generates spatially coherent ground motion across more than 9 million grid points in seconds, achieving a 10,000-fold speedup over the simulation workflow, which opens a path toward rapid and uncertainty-aware hazard assessment for distributed infrastructure. More broadly, GMFlow advances mesh-agnostic functional generative modeling and could potentially be extended to the synthesis of large-scale spatiotemporal physical fields in diverse scientific domains.