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Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks
The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards.
Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression
Unforeseen particle accelerator interruptions, also known as interlocks, lead to abrupt operational changes despite being necessary safety measures.
Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators
Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well as precisely defined control requirements.
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time.
