USING ARTIFICIAL NEURAL NETWORKS TO ACCELERATE TRANSPORT SOLVES

Mauricio E. Tano; Jean C. Ragusa

doi:10.1051/epjconf/202124703027

All issues

Volume 247 (2021)

EPJ Web Conf., 247 (2021) 03027

References

Open Access

Issue		EPJ Web Conf. Volume 247, 2021 PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future


Article Number		03027
Number of page(s)		8
Section		Deterministic Transport
DOI		https://doi.org/10.1051/epjconf/202124703027
Published online		22 February 2021

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[1] S. Raschka. “MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack.” J Open Source Software, volume 3(24), p. 638 (2018). [Google Scholar]

[2] E. E. Lewis and W. F. Miller. “Computational methods of neutron transport.” (1984). [Google Scholar]

[3] W. H. Reed, T. Hill, F. Brinkley, and K. Lathrop. “TRIPLET: A two-dimensional, multigroup, triangular mesh, planar geometry, explicit transport code.” Technical report, Los Alamos Scientific Lab., N. Mex.(USA) (1973). [Google Scholar]

[4] T. G¨uneysu and C. Paar. “Ultra high performance ECC over NIST primes on commercial FPGAs.” In International Workshop on Cryptographic Hardware and Embedded Systems, pp. 62–78. Springer (2008). [Google Scholar]

[5] M. Tano and J. Ragusa. “Acceleration of Radiation Transport Solves Using Artificial Neural Networks.” arXiv preprint arXiv:190604017 (2019). [Google Scholar]

[6] K. Kobayashi, N. Sugimura, and Y. Nagaya. 3-D radiation transport benchmark problems and results for simple geometries with void regions. Nuclear Energy Agency (2000). [Google Scholar]