Efficiency of machine learning optimizers and meta-optimization for nanophotonic inverse design tasks
The success of deep learning has driven the proliferation and refinement of numerous non-convex optimization algorithms. Despite this growing array of options, the field of nanophotonic inverse design continues to rely heavily on quasi-Newton optimizers such as L-BFGS and basic momentum-based method...
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| Published in: | APL machine learning Vol. 3; no. 1; pp. 016101 - 016101-13 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
AIP Publishing LLC
01.03.2025
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| ISSN: | 2770-9019, 2770-9019 |
| Online Access: | Get full text |
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