Physics model-informed Gaussian process for online optimization of particle accelerators
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 system. Typical GP models learn from past observations to make predictions, but this reduces their applicability to sys...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2021-07-01
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| Series: | Physical Review Accelerators and Beams |
| Online Access: | http://doi.org/10.1103/PhysRevAccelBeams.24.072802 |