Bayesian optimization algorithms for accelerator physics
Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …
accelerator control and tasks such as experimental design and model calibration in …
Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty
Q Wei, Y Wang, G Yang, T Li, S Yu, Z Dong… - npj Computational …, 2025 - nature.com
We present a multi-objective Bayesian active learning strategy, which greatly accelerates
the discovery of super high-strength and high-ductility lead-free solder alloys. The active …
the discovery of super high-strength and high-ductility lead-free solder alloys. The active …
Machine-learning-accelerated multi-objective design of fractured geothermal systems
Multi-objective optimization has burgeoned as a potent methodology for informed decision-
making in enhanced geothermal systems, aiming to concurrently maximize economic yield …
making in enhanced geothermal systems, aiming to concurrently maximize economic yield …
CBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning
Complex dynamical systems, such as particle accelerators, often require complicated and
time-consuming tuning procedures for optimal performance. It may also be required that …
time-consuming tuning procedures for optimal performance. It may also be required that …
Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators
Particle accelerator operation requires simultaneous optimization of multiple objectives.
Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the …
Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the …