Bayesian PINNs with Hamiltonian Monte Carlo sampling deliver the most consistent uncertainty estimates for turbulent flow inverse problems, while repulsive deep ensembles provide a faster but slightly less calibrated alternative.
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Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
Bayesian PINNs with Hamiltonian Monte Carlo sampling deliver the most consistent uncertainty estimates for turbulent flow inverse problems, while repulsive deep ensembles provide a faster but slightly less calibrated alternative.