PAS method estimates extreme non-linear wave impact loads within 2-15% of brute-force Monte Carlo results while using only 1-3% of the high-fidelity simulation time.
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WeatherRobustBus injects hourly weather into real bus blocks, couples a physics backbone with a bounded monotone residual ensemble, and shows a policy reducing cold-wave failure probability from 0.759 to 0.112.
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Design loads for wave impacts -- introducing the Probabilistic Adaptive Screening (PAS) method for predicting extreme non-linear loads on maritime structures
PAS method estimates extreme non-linear wave impact loads within 2-15% of brute-force Monte Carlo results while using only 1-3% of the high-fidelity simulation time.
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When the Timetable Breaks: Physics-Anchored Scientific Machine Learning for Cold-Wave-Robust Battery-Electric Bus Operations
WeatherRobustBus injects hourly weather into real bus blocks, couples a physics backbone with a bounded monotone residual ensemble, and shows a policy reducing cold-wave failure probability from 0.759 to 0.112.