Improved surrogates in inertial confinement fusion with manifold and cycle consistencies.
Proc Natl Acad Sci U S A
; 117(18): 9741-9746, 2020 05 05.
Article
en En
| MEDLINE
| ID: mdl-32312816
Neural networks have become the method of choice in surrogate modeling because of their ability to characterize arbitrary, high-dimensional functions in a data-driven fashion. This paper advocates for the training of surrogates that are 1) consistent with the physical manifold, resulting in physically meaningful predictions, and 2) cyclically consistent with a jointly trained inverse model; i.e., backmapping predictions through the inverse results in the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, are more resilient to sampling artifacts, and tend to be more data efficient. Using inertial confinement fusion (ICF) as a test-bed problem, we model a one-dimensional semianalytic numerical simulator and demonstrate the effectiveness of our approach.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Año:
2020
Tipo del documento:
Article
Pais de publicación:
Estados Unidos