151 citations · 345 across the 9 of their papers we have counts for
9 papers
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…
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…
Multi-Objective Bayesian Optimization for Accelerator Tuning
Ryan Roussel, Adi Hanuka, Auralee Edelen
Particle accelerators require constant tuning during operation to meet beam quality, total charge and particle energy requirements for use in a wide variety of physics, chemistry a…
Physics-informed Gaussian Process for Online Optimization of Particle Accelerators
Adi Hanuka, X. Huang, J. Shtalenkova +5
High-dimensional optimization is a critical challenge for operating large-scale scientific facilities. We apply a physics-informed Gaussian process (GP) optimizer to tune a complex…