Integrating Bayesian neural networks into differentiable hybrid PIML architectures yields uncertainty estimates with accuracy slightly worse than or equal to deterministic baselines.
Analyses of internal structures and defects in mate- rials using physics-informed neural networks,
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Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures
Integrating Bayesian neural networks into differentiable hybrid PIML architectures yields uncertainty estimates with accuracy slightly worse than or equal to deterministic baselines.