Cargando…
Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients
Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs...
Autores principales: | Prantikos, Konstantinos, Chatzidakis, Stylianos, Tsoukalas, Lefteri H., Heifetz, Alexander |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558465/ https://www.ncbi.nlm.nih.gov/pubmed/37803015 http://dx.doi.org/10.1038/s41598-023-43325-1 |
Ejemplares similares
-
Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery
por: Lee, Munhwan, et al.
Publicado: (2019) -
EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks
por: Herrero Martin, Clara, et al.
Publicado: (2022) -
Commentary: EP-PINNs: Cardiac electrophysiology characterisation using physics-informed neural networks
por: Meier, Stefan, et al.
Publicado: (2022) -
Dr. Vivian Pinn: a woman pioneer & leader
por: Bachmann, Gloria, et al.
Publicado: (2021) -
Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT)
por: Inda, Adan Jafet Garcia, et al.
Publicado: (2022)