Constrained Bayesian optimization of criticality experiments

Author:

Siefman D.,Percher C.,Norris J.,Kersting A.,Heinrichs D.

Funder

National Nuclear Security Administration

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference25 articles.

1. Brown, D.A. et al., 2018. ENDF/B-VIII.0: The 8th major release of the nuclear reaction data library with CIELO-project cross sections, new standards and thermal scattering data. Nuclear Data Sheets 148.

2. Gardner, J.R. et al., 2014. Bayesian optimization with inequality constraints. In: Proceedings of the 31st International Conference on Machine Learning, Beijing, China. JMLR: W&CP, vol. 32.

3. Classes of kernels for machine learning: A statistics perspective;Genton;Journal of Machine Learning Research,2002

4. Features of MCNP6;Goorley;Annals of Nuclear Energy,2016

5. Hutchinson, J., Arthur, J., Bahran, R., Pozzi, S., 2018. Bayesian optimization for subcritical benchmark design. In: American Nuclear Society Winter Meeting and Technology Expo, Orlando FL, USA.

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