ARTIFICIAL INTELLIGENCE DESIGN OF NUCLEAR SYSTEMS EMPOWERED BY ADVANCED MANUFACTURING

Vladimir Sobes; Briana Hiscox; Emilian Popov; Marco Delchini; Richard Archibald; Cory Hauck; Paul Laiu; Ben Betzler; Kurt Terrani

doi:10.1051/epjconf/202124706032

All issues

Volume 247 (2021)

EPJ Web Conf., 247 (2021) 06032

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		06032
Number of page(s)		8
Section		Advanced Modelling and Simulation
DOI		https://doi.org/10.1051/epjconf/202124706032
Published online		22 February 2021

T. M. Pandya, S. R. Johnson, T. M. Evans, G. G. Davidson, S. P. Hamilton and A. T. Godfrey, “Implementation, capabilities, and benchmarking of Shift, a massively parallel Monte Carlo radiation transport code,” Journal of Computational Physics, vol. 308, pp. 239-272, 2016. [Google Scholar]
S. J. Osher and R. P. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Springer-Verlag, 2002. [Google Scholar]
J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge: University Press, 1999. [Google Scholar]
D. Higdon, M. Kennedy, J. C. Cavendish, J. A. Cafeo and R. D. Ryne, “Combining field data and computer simulations for calibration and prediction,” SIAM Journal of Scientific Computing, vol. 26, pp. 448-446, 2004. [Google Scholar]
M. C. Kenned and A. O'Hagan, “Bayesian calibration of computer models (with Discussion),” Journal of the Royal Statistical Society - Series B, vol. 63, pp. 425-464, 2001. [Google Scholar]
C. E. Rasmussen and C. K. Williams, “Gaussian Processes for Machine Learning,” Adaptive Computation and Machine Learning, 2005. [Google Scholar]
T. G. Kolda, R. M. Lewis and V. Torczon, “Optimization by direct search: New perspectives on some classical and modern methods,” SIAM reivew, vol. 45, no. 3, pp. 385-482, 2008. [Google Scholar]
C. Audet and J. E. Dennis, “Analysis of generalized pattern searches,” SIAM Journal on optimizaiton, vol. 13, no. 3, pp. 889-903, 2002. [Google Scholar]
L. Eca and M. Hoekstra, “A procedure for the estiamtion of the numerical uncertainty of CFD calculations based on grid refinement studies,” Journal of Computational Physics, pp. 104-130, 2014. [Google Scholar]
S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” Journal of Compuational Physics, pp. 12-49, 1988. [Google Scholar]
Siemens, “STAR-CCM+ documentaion and code description,” [Online]. Available: https://www.plm.automation.siemens.com/global/en/products/simcenter/STAR-CCM.html. [Google Scholar]
M. B. Giles, “Multilevel Monte Carlo path simulation,” Operations Research, vol. 56, no. 3, pp. 607-617, 2008. [Google Scholar]
B. Peherstorfer, K. Willcox and M. Gunzberger, “Survey of multifidelity methods in uncertainty propagation, inference, and optimization,” Siam Review, vol. 60, no. 3, pp. 550-591, 2018. [Google Scholar]
V. Torczon, “On the convergence of pattern search algorithms,” SIAM Journal on optimization, vol. 7, no. 1, pp. 1-25, 1997. [Google Scholar]
J. Gero, “Design Optimization,” Academic Press, 1985. [Google Scholar]
O. Sigmund and K. Maute, “Topology optimization approaches, A compartive review,” Structural Multidisciplinary Optimization, vol. 48, pp. 1031-1055, 2013. [Google Scholar]

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[1] T. M. Pandya, S. R. Johnson, T. M. Evans, G. G. Davidson, S. P. Hamilton and A. T. Godfrey, “Implementation, capabilities, and benchmarking of Shift, a massively parallel Monte Carlo radiation transport code,” Journal of Computational Physics, vol. 308, pp. 239-272, 2016. [Google Scholar]

[2] S. J. Osher and R. P. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Springer-Verlag, 2002. [Google Scholar]

[3] J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge: University Press, 1999. [Google Scholar]

[4] D. Higdon, M. Kennedy, J. C. Cavendish, J. A. Cafeo and R. D. Ryne, “Combining field data and computer simulations for calibration and prediction,” SIAM Journal of Scientific Computing, vol. 26, pp. 448-446, 2004. [Google Scholar]

[5] M. C. Kenned and A. O'Hagan, “Bayesian calibration of computer models (with Discussion),” Journal of the Royal Statistical Society - Series B, vol. 63, pp. 425-464, 2001. [Google Scholar]

[6] C. E. Rasmussen and C. K. Williams, “Gaussian Processes for Machine Learning,” Adaptive Computation and Machine Learning, 2005. [Google Scholar]

[7] T. G. Kolda, R. M. Lewis and V. Torczon, “Optimization by direct search: New perspectives on some classical and modern methods,” SIAM reivew, vol. 45, no. 3, pp. 385-482, 2008. [Google Scholar]

[8] C. Audet and J. E. Dennis, “Analysis of generalized pattern searches,” SIAM Journal on optimizaiton, vol. 13, no. 3, pp. 889-903, 2002. [Google Scholar]

[9] L. Eca and M. Hoekstra, “A procedure for the estiamtion of the numerical uncertainty of CFD calculations based on grid refinement studies,” Journal of Computational Physics, pp. 104-130, 2014. [Google Scholar]

[10] S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” Journal of Compuational Physics, pp. 12-49, 1988. [Google Scholar]

[11] Siemens, “STAR-CCM+ documentaion and code description,” [Online]. Available: https://www.plm.automation.siemens.com/global/en/products/simcenter/STAR-CCM.html. [Google Scholar]

[12] M. B. Giles, “Multilevel Monte Carlo path simulation,” Operations Research, vol. 56, no. 3, pp. 607-617, 2008. [Google Scholar]

[13] B. Peherstorfer, K. Willcox and M. Gunzberger, “Survey of multifidelity methods in uncertainty propagation, inference, and optimization,” Siam Review, vol. 60, no. 3, pp. 550-591, 2018. [Google Scholar]

[14] V. Torczon, “On the convergence of pattern search algorithms,” SIAM Journal on optimization, vol. 7, no. 1, pp. 1-25, 1997. [Google Scholar]

[15] J. Gero, “Design Optimization,” Academic Press, 1985. [Google Scholar]

[16] O. Sigmund and K. Maute, “Topology optimization approaches, A compartive review,” Structural Multidisciplinary Optimization, vol. 48, pp. 1031-1055, 2013. [Google Scholar]