Matches in SemOpenAlex for { <https://semopenalex.org/work/W4289644906> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4289644906 abstract "Predictive and real-time inference capability for the upstream separatrix electron density, $n_text{e, sep}$, is essential for design and control of core-edge integrated plasma scenarios. In this study, both supervised and semi-supervised machine learning algorithms are explored to establish direct mapping as well as indirect compressed representation of the pedestal profiles for predictions and inference of $n_{text{e, sep}}$. Based on the EUROfusion pedestal database for JET, a tabular dataset was created, consisting of machine parameters, fraction of ELM cycle, high resolution Thomson scattering profiles of electron density and temperature, and $n_{text{e, sep}}$ for 608 JET shots. Using the tabular dataset, the direct mapping approach provides a mapping of machine parameters and ELM percentage to $n_{text{e, sep}}$. Through representation learning, a compressed representation of the experimental pedestal electron density and temperature profiles is established. By conditioning the representation with machine control parameters, a probabilistic generative predictive model is established. For prediction, the machine parameters can be used to establish a conditional distribution of the compressed pedestal profiles, and the decoder that is trained as part of the algorithm can be used to decode the compressed representation back to full pedestal profiles. Although, in this work, a proof-of-principle for predicting and inferring $n_{text{e, sep}}$ is given, such a representation learning can be used also for many other applications as the full pedestal profile is predicted. An implementation of this work can be found at https://github.com/fusionby2030/moxie." @default.
- W4289644906 created "2022-08-03" @default.
- W4289644906 creator A5011245232 @default.
- W4289644906 creator A5029561131 @default.
- W4289644906 creator A5038021575 @default.
- W4289644906 creator A5074209978 @default.
- W4289644906 creator A5089562711 @default.
- W4289644906 date "2022-07-30" @default.
- W4289644906 modified "2023-09-24" @default.
- W4289644906 title "Developing Deep Learning Algorithms for Inferring Upstream Separatrix Density at JET" @default.
- W4289644906 doi "https://doi.org/10.48550/arxiv.2208.00206" @default.
- W4289644906 hasPublicationYear "2022" @default.
- W4289644906 type Work @default.
- W4289644906 citedByCount "0" @default.
- W4289644906 crossrefType "posted-content" @default.
- W4289644906 hasAuthorship W4289644906A5011245232 @default.
- W4289644906 hasAuthorship W4289644906A5029561131 @default.
- W4289644906 hasAuthorship W4289644906A5038021575 @default.
- W4289644906 hasAuthorship W4289644906A5074209978 @default.
- W4289644906 hasAuthorship W4289644906A5089562711 @default.
- W4289644906 hasBestOaLocation W42896449061 @default.
- W4289644906 hasConcept C11413529 @default.
- W4289644906 hasConcept C119857082 @default.
- W4289644906 hasConcept C119947313 @default.
- W4289644906 hasConcept C121332964 @default.
- W4289644906 hasConcept C127413603 @default.
- W4289644906 hasConcept C154945302 @default.
- W4289644906 hasConcept C162307627 @default.
- W4289644906 hasConcept C17744445 @default.
- W4289644906 hasConcept C199539241 @default.
- W4289644906 hasConcept C2776214188 @default.
- W4289644906 hasConcept C2776359362 @default.
- W4289644906 hasConcept C2778611943 @default.
- W4289644906 hasConcept C41008148 @default.
- W4289644906 hasConcept C49937458 @default.
- W4289644906 hasConcept C57879066 @default.
- W4289644906 hasConcept C78519656 @default.
- W4289644906 hasConcept C94625758 @default.
- W4289644906 hasConceptScore W4289644906C11413529 @default.
- W4289644906 hasConceptScore W4289644906C119857082 @default.
- W4289644906 hasConceptScore W4289644906C119947313 @default.
- W4289644906 hasConceptScore W4289644906C121332964 @default.
- W4289644906 hasConceptScore W4289644906C127413603 @default.
- W4289644906 hasConceptScore W4289644906C154945302 @default.
- W4289644906 hasConceptScore W4289644906C162307627 @default.
- W4289644906 hasConceptScore W4289644906C17744445 @default.
- W4289644906 hasConceptScore W4289644906C199539241 @default.
- W4289644906 hasConceptScore W4289644906C2776214188 @default.
- W4289644906 hasConceptScore W4289644906C2776359362 @default.
- W4289644906 hasConceptScore W4289644906C2778611943 @default.
- W4289644906 hasConceptScore W4289644906C41008148 @default.
- W4289644906 hasConceptScore W4289644906C49937458 @default.
- W4289644906 hasConceptScore W4289644906C57879066 @default.
- W4289644906 hasConceptScore W4289644906C78519656 @default.
- W4289644906 hasConceptScore W4289644906C94625758 @default.
- W4289644906 hasLocation W42896449061 @default.
- W4289644906 hasOpenAccess W4289644906 @default.
- W4289644906 hasPrimaryLocation W42896449061 @default.
- W4289644906 hasRelatedWork W10600273 @default.
- W4289644906 hasRelatedWork W11144228 @default.
- W4289644906 hasRelatedWork W221938 @default.
- W4289644906 hasRelatedWork W4313108 @default.
- W4289644906 hasRelatedWork W4529005 @default.
- W4289644906 hasRelatedWork W4564978 @default.
- W4289644906 hasRelatedWork W4828744 @default.
- W4289644906 hasRelatedWork W731497 @default.
- W4289644906 hasRelatedWork W7699138 @default.
- W4289644906 hasRelatedWork W7890312 @default.
- W4289644906 isParatext "false" @default.
- W4289644906 isRetracted "false" @default.
- W4289644906 workType "article" @default.