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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.17403 |
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| _version_ | 1866911525150654464 |
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| author | Shi, Yaozhong Lavrentiadis, Grigorios Tsalouchidis, Konstantinos Ross, Zachary E. McCallen, David Zou, Caifeng Azizzadenesheli, Kamyar Asimaki, Domniki |
| author_facet | Shi, Yaozhong Lavrentiadis, Grigorios Tsalouchidis, Konstantinos Ross, Zachary E. McCallen, David Zou, Caifeng Azizzadenesheli, Kamyar Asimaki, Domniki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17403 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching Shi, Yaozhong Lavrentiadis, Grigorios Tsalouchidis, Konstantinos Ross, Zachary E. McCallen, David Zou, Caifeng Azizzadenesheli, Kamyar Asimaki, Domniki Machine Learning 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. |
| title | Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.17403 |