activity
20212023
most citedFast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks

2 citations · 3 across the 4 of their papers we have counts for

collaborators
Showing 2023Show all

2 papers · 1 filter

physics.comp-ph2023★ 1 cited

Uncertainty Quantification on Spent Nuclear Fuel with LMC

Arnau Albà, Andreas Adelmann, Dimitri Rochman

The recently developed method Lasso Monte Carlo (LMC) for uncertainty quantification is applied to the characterisation of spent nuclear fuel. The propagation of nuclear data uncer…

cs.LG2023★ 2 cited

Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks

Arnau Albà, Andreas Adelmann, Lucas Münster +2

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainabilit…