Mr. Nathanael Hulard Profile

Nathanael Hulard

at ONERA

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 8 March 2023 Presentation + Paper

Deep-learning applied to coherent combining of fiber lasers: from numerical modelling to experimental demonstration

P. Bourdon, N. Hulard, F. Gustave, A. Liméry, D. Goular, C. Planchat, L. Lombard

Proceedings Volume 12400, 124000C (2023) https://doi.org/10.1117/12.2648843

KEYWORDS: Fiber lasers, Phase retrieval, Signal processing, Image processing, Neural networks, Image retrieval, Numerical modeling, Education and training, Cameras, Laser frequency

Read Abstract +

Coherent beam combining (CBC) by active phase control is an efficient way to power scale fiber amplifiers but its bandwidth of operation of CBC can be limited. Deep-learning techniques offer some capability for fast retrieval of the laser phases from the shape of the interference pattern generated through combining, in order to increase the speed and bandwidth of operation of CBC. In this paper, we present the development and numerical tests of a Convolutional Neural Network (CNN) used for such fast phase retrieval. After numerically generating tens of thousands of interference patterns corresponding to different phase sets for the combined lasers, we learned the CNN to retrieve the phase set corresponding to a given shape of interference pattern. Unfortunately, due to the central symmetry of the tiled-aperture hexagonal geometry of the array of fiber outputs, there’s not a unique set of phases for the combined lasers that can lead to a given shape of interference pattern. We demonstrate that acquiring the image of the interference pattern in a plane that is not perfectly located in the far-field offers a simple solution to get rid of this non-uniqueness ambiguity. After demonstrating numerically that with this addition, the CNN learning approach operates well resulting in low values for the CBC residual phase error, we explain how it’s possible to transfer this learning that has been done numerically to a real experiment.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years