NEUP CINR-24-31807: Concurrent Surrogate Model Development with Uncertainty Quantification in the MOOSE Framework Using Physics-Informed Gaussian-Process Machine Learning

The objective of this project is to develop a general capability for concurrent generation and use of physics-informed Gaussian process (GP)-based surrogate models to facilitate...

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