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 (Developer)
, Anil Yayak (Developer)
, Katrin Wudy (Developer)
, Anoush Aghajani-Talesh (Developer)
, Markus Birg (Developer)
, Jonas Grünewald (Developer)
, Mike J. Holenderski
, Nirvana Meratnia

Research output: Non-textual form › Software › Academic

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.

Original language	English
Publisher	Optica Publishing Group
Edition	Version 2
Media of output	Online
Size	254 MB
DOIs	https://doi.org/10.6084/m9.figshare.27650946.v2
Publication status	Published - 21 Oct 2025

Bibliographical 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

Funding

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

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

Promotion : time and place

03/03/2025
Eindhoven, Netherlands

Access to Document

10.6084/m9.figshare.27650946.v2Licence: CC BY

Cite this

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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",

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

language = "English",

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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

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Promotion : time and place

Access to Document

Other files and links

Fingerprint

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

Cite this