A polynomial chaos approach for uncertainty quantification of Monte Carlo transport codes
<|reference_start|>A polynomial chaos approach for uncertainty quantification of Monte Carlo transport codes: In this contribution, we discuss the construction of Polynomial Chaos surrogates for Monte Carlo radiation transport applications via non-intrusive spectral projection. This contribution focuses on improvements with respect to the approach that we previously introduced in previous work. We focus on understanding the impact of re-sampling cost on the algorithm performance and provide algorithm refinements, which allow to obtain unbiased estimators for the variance, estimate the PC variability due to limited samples, and adapt the expansion. An attenuation-only test case is provided to illustrate and discuss the results.<|reference_end|>
@article{geraci2024a,
    title={A polynomial chaos approach for uncertainty quantification of Monte
  Carlo transport codes},
    author={Gianluca Geraci, Kayla Clements, Aaron J Olson},
    journal={Proceedings of The International Conference on Mathematics and
  Computational Methods Applied to Nuclear Science and Engineering, 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07024},
    primaryClass={math.NA cs.NA math.ST stat.TH}
}