Bayesian optimization algorithms for accelerator physics

R Roussel, AL Edelen, T Boltz, D Kennedy… - … review accelerators and …, 2024 - APS
Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …

Generalizing Bayesian optimization with decision-theoretic entropies

W Neiswanger, L Yu, S Zhao… - Advances in Neural …, 2022 - proceedings.neurips.cc
Bayesian optimization (BO) is a popular method for efficiently inferring optima of an
expensive black-box function via a sequence of queries. Existing information-theoretic BO …

Targeted materials discovery using Bayesian algorithm execution

SR Chitturi, A Ramdas, Y Wu, B Rohr… - npj Computational …, 2024 - nature.com
Rapid discovery and synthesis of future materials requires intelligent data acquisition
strategies to navigate large design spaces. A popular strategy is Bayesian optimization …

arXiv: Bayesian Optimization Algorithms for Accelerator Physics

R Roussel, AS Garcia, W Lin, T Boltz, J Kaiser… - 2023 - cds.cern.ch
Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …

Data-Driven Techniques for Materials Characterization and Intelligent Experimental Design at Advanced Scattering Facilities

SR Chitturi - 2024 - search.proquest.com
This dissertation addresses the need to accelerate materials discovery, particularly within
the context of advanced scattering facilities. The complexity of materials discovery arises …

[图书][B] Data-Driven Sequential Decision Making with Deep Probabilistic Modeling

L Yu - 2022 - search.proquest.com
A central ability of intelligent agents is learning by interacting with the surrounding
environment. Bayesian optimization (BO) and reinforcement learning (RL) provide …