Towards a neural network based flux density prediction – Using generative models to enhance CSP raytracing
Towards a neural network based flux density prediction – Using generative models to enhance CSP raytracing
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Personal Name(s): | Pargmann, Max (Corresponding author) |
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Quinto, Daniel Maldonado / Kesselheim, Stefan / Ebert, Jan / Pitz-Paal, Robert | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
AIP Publishing
2023
|
Physical Description: |
030015-1–030015-12 |
DOI: |
10.1063/5.0148765 |
DOI: |
10.34734/FZJ-2024-03074 |
Conference: | THE INTERNATIONAL CONFERENCE ON BATTERY FOR RENEWABLE ENERGY AND ELECTRIC VEHICLES (ICB-REV) 2022, South Tangerang (Indonesia), 2021-09-27 - 2021-10-01 |
Document Type: |
Contribution to a conference proceedings |
Research Program: |
Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2024-03074 in citations.
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