| Autor: |
Whyte Andy, Parks Geoff |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
EPJ Web of Conferences, Vol 247, p 12003 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2100-014X |
| DOI: |
10.1051/epjconf/202124712003 |
| Popis: |
This paper investigates the applicability of surrogate model optimization (SMO) using deep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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