A unified framework with FPI-BPINN and fParVI-PINN approaches enables functional priors in Bayesian PINN inversion, yielding accurate posterior estimates for 1D seismic tomography and 2D Darcy flow permeability inversion.
arXiv preprint arXiv:2505.17308
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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.
citing papers explorer
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Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
A unified framework with FPI-BPINN and fParVI-PINN approaches enables functional priors in Bayesian PINN inversion, yielding accurate posterior estimates for 1D seismic tomography and 2D Darcy flow permeability inversion.
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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.