Simulation, deep learning, and simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems

Shengyuan Yan; Richard Off; Anil Yayak; Katrin Wudy; Anoush Aghajani-Talesh; Markus Birg; Jonas Grünewald; Mike J. Holenderski; Nirvana Meratnia

doi:10.6084/m9.figshare.27650946.v2

Simulation, deep learning, and simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems

Shengyuan Yan
, Richard Off (Ontwikkelaar)
, Anil Yayak (Ontwikkelaar)
, Katrin Wudy (Ontwikkelaar)
, Anoush Aghajani-Talesh (Ontwikkelaar)
, Markus Birg (Ontwikkelaar)
, Jonas Grünewald (Ontwikkelaar)
, Mike J. Holenderski
, Nirvana Meratnia

Onderzoeksoutput: Niet-tekstuele vorm › computerprogramma software › Academic

Samenvatting

This code file contains the codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies in our paper "Deep learning based phase retrieval with complex beam shapes for beam shape correction". After unzipping the file, the directory contains 4 subfolder for simulation codes, deep learning codes, simulation-assisted analysis codes, and statistical figure plotting codes, respectively. In each subfolder there is a readme file. Follow the instructions in the readme file to use the codes in the subfolder please.

Originele taal-2	Engels
Uitgever	Optica Publishing Group
Uitgave	Version 2
Outputmedia	Online
Grootte	254 MB
DOI's	https://doi.org/10.6084/m9.figshare.27650946.v2
Status	Gepubliceerd - 21 okt. 2025

Bibliografische nota

Official title according to the DOI : Codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems

Financiering

This work was supported by the EU InShaPe project funded by the European Union (EU Funding Nr.: 101058523 — InShaPe).

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10.6084/m9.figshare.27650946.v2Licentie: CC BY

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Github repository

Deep learning based phase retrieval with complex beam shapes for beam shape correction
Yan, S. (Corresponding author), Off, R., Yayak, A., Wudy, K., Aghajani-Talesh, A., Birg, M., Grünewald, J., Holenderski, M. J. & Meratnia, N., 10 mrt. 2025, In: Optics Express. 33, 5, blz. 10806-10834 29 blz.
Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

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Yan, S., Off, R., Yayak, A., Wudy, K., Aghajani-Talesh, A., Birg, M., Grünewald, J., Holenderski, M. J., & Meratnia, N. (2025). Simulation, deep learning, and simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems. computerprogramma software, Optica Publishing Group. https://doi.org/10.6084/m9.figshare.27650946.v2

@misc{fff3b22115eb4e5d8bc6267c445513ff,

title = "Simulation, deep learning, and simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems",

abstract = "This code file contains the codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies in our paper {"}Deep learning based phase retrieval with complex beam shapes for beam shape correction{"}. After unzipping the file, the directory contains 4 subfolder for simulation codes, deep learning codes, simulation-assisted analysis codes, and statistical figure plotting codes, respectively. In each subfolder there is a readme file. Follow the instructions in the readme file to use the codes in the subfolder please.",

keywords = "Fourier Optics, Image and Signal Processing, Beam shaping, aberration amplitude, beam shape correction, deep learning, Simulation Assisted Analysis",

author = "Shengyuan Yan and Richard Off and Anil Yayak and Katrin Wudy and Anoush Aghajani-Talesh and Markus Birg and Jonas Gr{\"u}newald and Holenderski, \{Mike J.\} and Nirvana Meratnia",

note = "Official title according to the DOI : Codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems",

year = "2025",

month = oct,

day = "21",

doi = "10.6084/m9.figshare.27650946.v2",

language = "English",

publisher = "Optica Publishing Group",

address = "United States",

edition = "Version 2",

}

TY - ADVS

T1 - Simulation, deep learning, and simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems

AU - Yan, Shengyuan

AU - Holenderski, Mike J.

AU - Meratnia, Nirvana

A2 - Off, Richard

A2 - Yayak, Anil

A2 - Wudy, Katrin

A2 - Aghajani-Talesh, Anoush

A2 - Birg, Markus

A2 - Grünewald, Jonas

N1 - Official title according to the DOI : Codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems

PY - 2025/10/21

Y1 - 2025/10/21

N2 - This code file contains the codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies in our paper "Deep learning based phase retrieval with complex beam shapes for beam shape correction". After unzipping the file, the directory contains 4 subfolder for simulation codes, deep learning codes, simulation-assisted analysis codes, and statistical figure plotting codes, respectively. In each subfolder there is a readme file. Follow the instructions in the readme file to use the codes in the subfolder please.

AB - This code file contains the codes for optical simulation, deep learning models training and testing, and the simulation-assisted analysis of aberration detection and beam shape correction accuracies in our paper "Deep learning based phase retrieval with complex beam shapes for beam shape correction". After unzipping the file, the directory contains 4 subfolder for simulation codes, deep learning codes, simulation-assisted analysis codes, and statistical figure plotting codes, respectively. In each subfolder there is a readme file. Follow the instructions in the readme file to use the codes in the subfolder please.

KW - Fourier Optics, Image and Signal Processing

KW - Beam shaping

KW - aberration amplitude

KW - beam shape correction

KW - deep learning

KW - Simulation Assisted Analysis

UR - https://github.com/yanshengyuan/Deep_Phase_Retrieval_with_PBF-LB-M_Complex_BeamShapes

U2 - 10.6084/m9.figshare.27650946.v2

DO - 10.6084/m9.figshare.27650946.v2

M3 - Software

PB - Optica Publishing Group

ER -

Simulation, deep learning, and simulation-assisted analysis of aberration detection and beam shape correction accuracies for PBF-LB/M systems

Samenvatting

Bibliografische nota

Financiering

Duurzame ontwikkelingsdoelstellingen van de VN

Toegang tot document

Andere bestanden en links

Vingerafdruk

Onderzoekersoutput

Deep learning based phase retrieval with complex beam shapes for beam shape correction

Citeer dit