Bayesian Optimization for Data Selection in Bragg Grating Design Space for Machine Learning Training
M.R. Mahani, I.A. Nechepurenko, Y. Rahimof, A. Wicht
Published in:
Conf. on Lasers and Electro-Optics/Europe and European Quantum Electronics Conf. (CLEO/Europe-EQEC 2025), Munich, Germany, Jun. 23-27, ISBN: 979-8-3315-1252-1, ce-p-15 (2025).
Abstract:
Data acquisition for training machine learning (ML) models in resource-intensive scientific fields is challenging [1]. Recognizing the high computational costs associated with generating extensive databases for training, we explore a Bayesian optimization (BO) approach to develop a minimal yet highly informative database. We aim to streamline data collection, reduce computational demands, and improve ML model performance in highdimensional parameter spaces.
Ferdinand-Braun-Institut (FBH), Gustav-Kirchhoff-Str. 4, 12489 Berlin, Germany
Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For reprint or republication permission, email to IEEE Copyrights Manager at pubs-permissions@ieee.org. All rights reserved. Copyright ©2025 by IEEE.
Rightslink® by Copyright Clearance Center
Full version in pdf-format.