Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Vladimir Sobes"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a
Externí odkaz:
https://doaj.org/article/6f355fd51bd141d9a9882e3b712b7425
Autor:
Vladimir Sobes, Briana Hiscox, Emilian Popov, Rick Archibald, Cory Hauck, Ben Betzler, Kurt Terrani
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Abstract The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature p
Externí odkaz:
https://doaj.org/article/5cc19e46f7d146e78fd797a7dbd0cc5e
Autor:
Karolina Kolos, Vladimir Sobes, Ramona Vogt, Catherine E. Romano, Michael S. Smith, Lee A. Bernstein, David A. Brown, Mary T. Burkey, Yaron Danon, Mohamed A. Elsawi, Bethany L. Goldblum, Lawrence H. Heilbronn, Susan L. Hogle, Jesson Hutchinson, Ben Loer, Elizabeth A. McCutchan, Matthew R. Mumpower, Ellen M. O'Brien, Catherine Percher, Patrick N. Peplowski, Jennifer J. Ressler, Nicolas Schunck, Nicholas W. Thompson, Andrew S. Voyles, William Wieselquist, Michael Zerkle
Publikováno v:
Physical Review Research, Vol 4, Iss 2, p 021001 (2022)
Accurate nuclear data provide an essential foundation for advances in a wide range of fields, including nuclear energy, nuclear safety and security, safeguards, nuclear medicine, and planetary and space exploration. In these and other critical domain
Externí odkaz:
https://doaj.org/article/3a13011004af404cb91cfa3fa6acca57
Publikováno v:
Nuclear Science and Engineering. :1-8
Autor:
J. Wesley Hines, Austin M. Williams, John Pevey, Vladimir Sobes, Briana Hiscox, Ondřej Chvála
Publikováno v:
Nuclear Science and Engineering. 196:1559-1571
The use of gradient descent methods for optimizing k-eigenvalue nuclear system has been shown to be useful in the past, but the k-eigenvalue gradients have proved challenging due to their stochastic nature and uncertainty. ADAM is a gradient descent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a4794349b16d8fb54cb3c53508db7fb
https://doi.org/10.21203/rs.3.rs-2073277/v1
https://doi.org/10.21203/rs.3.rs-2073277/v1
Publikováno v:
Nuclear Science and Engineering. 195:813-824
A benchmark to verify the accuracy of neutron transport criticality solvers along the energy dimension was established. For the first time, the analytic solution of the flux amplitude was derived i...
Autor:
Jesse M. Brown, Goran Arbanas, Dorothea Wiarda, Klaus H. Guber, Andrew Holcomb, Vladimir Sobes
Publikováno v:
EPJ Web of Conferences. 284:12003
Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear appro
Publikováno v:
EPJ Web of Conferences. 284:16004
This work serves as a proof of concept for an automated tool to assist in the evaluation of experimental neutron cross section data in the resolved resonance range. The resonance characterization problem is posed as a mixed integer nonlinear program
Autor:
Pablo Ducru, Vladimir Sobes
Publikováno v:
Physical Review C. 105