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Artificial Neural Networks as Surrogate Models for Uncertainty Quantification and Data Assimilation in 2-D/3-D Fuel Performance Studies

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Artificial Neural Networks as Surrogate Models for Uncertainty Quantification and Data Assimilation in 2-D/3-D Fuel Performance Studies

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Atomic level insight into irradiation effects in nuclear fuel materials

Expectation consistency for calibration of neural networks

Monte Carlo analysis of BWR geometries using a cycle check-up methodology

On double-descent in uncertainty quantification in overparametrized models

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Development of a Novel Finite Volume Methodology for Multi-Dimensional Fuel Performance Applications

Data assimilation of post irradiation examination experiments to adjust fission yields

Data assimilation of post-irradiation examination data for fission yields from GEF