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Found 3 papers, 2 papers with code
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
In this paper, we present an open-source Python library (https://github. com/Crunch-UQ4MI), termed NeuralUQ and accompanied by an educational tutorial, for employing UQ methods for SciML in a convenient and structured manner.
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods.
Meta-learning PINN loss functions
In the computational examples, the meta-learned losses are employed at test time for addressing regression and PDE task distributions.
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