Integrating Bayesian neural networks into differentiable hybrid PIML architectures yields uncertainty estimates with accuracy slightly worse than or equal to deterministic baselines.
A differentiable physics-informed machine learn- ing approach to model laser-based micro-manufacturing process,
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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.