Zobrazeno 1 - 10
of 253
pro vyhledávání: '"Shirvan, Koroush"'
Autor:
Wainwright, Haruko M., Christiaen, Chloe, Atz, Milos, Tchakerian, John Sebastian, Yu, Jiankai, Ridley, Gavin Keith, Shirvan, Koroush
This study presents a cross-disciplinary reactor-to-repository framework to compare different advanced reactors with respect to their spent nuclear fuel (SNF). The framework consists of (1) OpenMC for simulating neutronics, fuel depletion, and radioa
Externí odkaz:
http://arxiv.org/abs/2405.12805
Autor:
Seurin, Paul, Shirvan, Koroush
Optimizing the fuel cycle cost through the optimization of nuclear reactor core loading patterns involves multiple objectives and constraints, leading to a vast number of candidate solutions that cannot be explicitly solved. To advance the state-of-t
Externí odkaz:
http://arxiv.org/abs/2402.11040
Autor:
Seurin, Paul, Shirvan, Koroush
A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evaluation of candidate solutions ca
Externí odkaz:
http://arxiv.org/abs/2312.10194
Autor:
Seurin, Paul, Shirvan, Koroush
The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization. It is also characterized by multiple objectives and constraints, which makes it impossible to solve explicitly. Stochastic optimizat
Externí odkaz:
http://arxiv.org/abs/2305.05812
A data-driven framework for spatial-temporal prediction is proposed for reducing the computational cost of industrial thermal striping applications. The framework aims to efficiently identify the flow features and utilize them in spatiotemporal field
Externí odkaz:
http://arxiv.org/abs/2301.05667
Publikováno v:
In Nuclear Engineering and Design November 2024 428
Autor:
Seurin, Paul, Shirvan, Koroush
Publikováno v:
In Annals of Nuclear Energy 15 September 2024 205
Autor:
Radaideh, Majdi I., Du, Katelin, Seurin, Paul, Seyler, Devin, Gu, Xubo, Wang, Haijia, Shirvan, Koroush
We present an open-source Python framework for NeuroEvolution Optimization with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology. NEORL offers a global optimization interface of state-of-the-art algorithms in the
Externí odkaz:
http://arxiv.org/abs/2112.07057
Autor:
Halimi, Assil, Shirvan, Koroush
Publikováno v:
In Annals of Nuclear Energy 15 December 2024 209
Autor:
Seurin, Paul, Shirvan, Koroush
Publikováno v:
In Annals of Nuclear Energy 1 December 2024 208