151 citations · 345 across the 9 of their papers we have counts for
3 papers · 2 filters
Quantifying Uncertainty for Machine Learning Based Diagnostic
Owen Convery, Lewis Smith, Yarin Gal +1
Virtual Diagnostic (VD) is a deep learning tool that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the output is invasive, limite…
Turn-Key Constrained Parameter Space Exploration for Particle Accelerators Using Bayesian Active Learning
Ryan Roussel, Juan Pablo Gonzalez-Aguilera, Young-Kee Kim +6
Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input par…
Uncertainty Quantification for Virtual Diagnostic of Particle Accelerators
Owen Convery, Lewis Smith, Yarin Gal +1
Virtual Diagnostic (VD) is a computational tool based on deep learning that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the out…