Phase identification and bespoke beam shaping for coherent beam combination via deep learning
Phase identification and bespoke beam shaping for coherent beam combination via deep learning
Practical application of coherent beam combination of multiple fibres necessitates phase identification and optimisation in real-time. Here, we solve this mathematical challenge via deep learning, and hence demonstrate the potential for real-time bespoke beam shaping.
Mills, Benjamin
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Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
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Eason, R.W.
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Nilsson, Johan
f41d0948-4ca9-4b93-b44d-680ca0bf157b
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
19 May 2022
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Nilsson, Johan
f41d0948-4ca9-4b93-b44d-680ca0bf157b
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Benjamin, Grant-Jacob, James, Praeger, Matthew, Eason, R.W., Nilsson, Johan and Zervas, Michael N.
(2022)
Phase identification and bespoke beam shaping for coherent beam combination via deep learning.
CLEO, , San Jose, United States.
15 - 20 May 2022.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Practical application of coherent beam combination of multiple fibres necessitates phase identification and optimisation in real-time. Here, we solve this mathematical challenge via deep learning, and hence demonstrate the potential for real-time bespoke beam shaping.
Text
CLEO 2022 abstract CBC v09 -- PDF
- Accepted Manuscript
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Published date: 19 May 2022
Venue - Dates:
CLEO, , San Jose, United States, 2022-05-15 - 2022-05-20
Identifiers
Local EPrints ID: 457915
URI: http://eprints.soton.ac.uk/id/eprint/457915
PURE UUID: 71488d3a-116b-40e9-bb3b-732860df99a9
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Date deposited: 22 Jun 2022 16:33
Last modified: 17 Mar 2024 03:22
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Contributors
Author:
Benjamin Mills
Author:
James Grant-Jacob
Author:
Matthew Praeger
Author:
R.W. Eason
Author:
Johan Nilsson
Author:
Michael N. Zervas
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