Ray-level deep-learning-based correction for real-time flux density predictions

Diaz Alonso, Sergio and Raeder, Christian and Charly, Allen and Hoffschmidt, Bernhard (2025) Ray-level deep-learning-based correction for real-time flux density predictions. Results in Engineering. Elsevier. ISSN 2590-1230. (Submitted)

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Item URL in elib:

https://elib.dlr.de/218166/

Document Type:

Article

Title:

Ray-level deep-learning-based correction for real-time flux density predictions

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Diaz Alonso, Sergio	sergio.diazalonso (at) dlr.de	https://orcid.org/0009-0001-1283-5502	UNSPECIFIED
Raeder, Christian	Christian.Raeder (at) dlr.de	https://orcid.org/0009-0009-4903-6200	UNSPECIFIED
Charly, Allen	RWTH Aachen	UNSPECIFIED	UNSPECIFIED
Hoffschmidt, Bernhard	Bernhard.Hoffschmidt (at) dlr.de	UNSPECIFIED	UNSPECIFIED

Date:

2025

Journal or Publication Title:

Results in Engineering

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

Yes

In SCOPUS:

Yes

In ISI Web of Science:

Yes

Publisher:

Elsevier

ISSN:

2590-1230

Status:

Submitted

Keywords:

Concentrating solar technologies (CST); Deep learning; Graphic neural networks (GNN); Flux density predictions; Real-time simulation

HGF - Research field:

Energy

HGF - Program:

Materials and Technologies for the Energy Transition

HGF - Program Themes:

High-Temperature Thermal Technologies

DLR - Research area:

Energy

DLR - Program:

E SW - Solar and Wind Energy

DLR - Research theme (Project):

E - Smart Operation

Location:

Jülich

Institutes and Institutions:

Institute of Solar Research > Concentrating Solar Technologies

Deposited By:

Diaz Alonso, Sergio

Deposited On:

30 Oct 2025 09:25

Last Modified:

30 Oct 2025 09:25

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