A geometry-aware per-surface neural network surrogate predicts real-time hydrodynamic forces for amphibious AGVs from SDF submergence data, achieving low sMAPE on held-out CFD and reproducing quadratic drag and linear buoyancy scaling in full-scale vehicle trials without explicit encoding in theloss
Criterion of vehicle stability in floodwaters based on theoretical and experimental studies
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Geometry-Aware Surrogate for Real-Time Hydrodynamics Estimation of Autonomous Ground Vehicles in Amphibious Environments
A geometry-aware per-surface neural network surrogate predicts real-time hydrodynamic forces for amphibious AGVs from SDF submergence data, achieving low sMAPE on held-out CFD and reproducing quadratic drag and linear buoyancy scaling in full-scale vehicle trials without explicit encoding in theloss