A Bayesian PINN with posterior-variance-based pseudo labeling stabilizes PINN training and outperforms the ensemble baseline on six of eight benchmark PDE problems.
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo,
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B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling
A Bayesian PINN with posterior-variance-based pseudo labeling stabilizes PINN training and outperforms the ensemble baseline on six of eight benchmark PDE problems.