Accelerating uncertainty quantifiations in geohazard analysis
Physics-aware AI simulators to accelerate probablistic analysis in geohazards
Geotechnical systems are inherently uncertain due to the spatial variability of geomaterials, requiring probabilistic simulations for reliable analysis and design. We develop Random Graph Network Simulators (RGNS) to accelerate stochastic simulations by learning the underlying physics of geomechanical processes from high-fidelity numerical models. This framework enables efficient uncertainty quantification, reliability analysis, and probabilistic hazard assessment that would otherwise be computationally prohibitive.
Probabilistic landslide hazard zoning based on exceedance probability of post-failure distance.
References
2026
- Accelerating stochastic simulation of post-failure landslide runout using a random graph neural network-based simulatorComputers and Geotechnics, 2026