Photonic neural networks at the edge of spatiotemporal chaos in multimode fibers
Gespeichert in:
| Veröffentlicht in: | Nanophotonics vol. 14, no. 16 (2025), p. 2723 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | |
| Veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagworte: | |
| Online-Zugang: | Citation/Abstract Full Text - PDF |
| Tags: |
Keine Tags, Fügen Sie das erste Tag hinzu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3238363813 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2192-8606 | ||
| 022 | |a 2192-8614 | ||
| 024 | 7 | |a 10.1515/nanoph-2024-0593 |2 doi | |
| 035 | |a 3238363813 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 263855 |2 nlm | ||
| 100 | 1 | |a Bahadır Utku Kesgin |u Department of Electrical and Electronics Engineering, Koç University, Istanbul, Turkey | |
| 245 | 1 | |a Photonic neural networks at the edge of spatiotemporal chaos in multimode fibers | |
| 260 | |b Walter de Gruyter GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Optical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency. Optical systems offer promising alternatives to digital neural networks by exploiting light’s parallelism. This study explores a photonic neural network design using spatiotemporal chaos within graded-index multimode fibers to improve machine learning performance. Through numerical simulations and experiments, we show that chaotic light propagation in multimode fibers enhances data classification accuracy across domains, including biomedical imaging, fashion, and satellite geospatial analysis. This chaotic optical approach enables high-dimensional transformations, amplifying data separability and differentiation for greater accuracy. Fine-tuning parameters such as pulse peak power optimizes the reservoir’s chaotic properties, highlighting the need for careful calibration. These findings underscore the potential of chaos-based nonlinear photonic neural networks to advance optical computing in machine learning, paving the way for efficient, scalable architectures. | |
| 653 | |a Accuracy | ||
| 653 | |a Computation | ||
| 653 | |a Data processing | ||
| 653 | |a Spatial analysis | ||
| 653 | |a Photonics | ||
| 653 | |a Neural networks | ||
| 653 | |a Machine learning | ||
| 653 | |a Network design | ||
| 653 | |a Medical imaging | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Propagation | ||
| 653 | |a Simulation | ||
| 653 | |a Datasets | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Lasers | ||
| 653 | |a Experiments | ||
| 653 | |a Numerical analysis | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Light | ||
| 653 | |a Optics | ||
| 700 | 1 | |a Teğin, Uğur |u Department of Electrical and Electronics Engineering, Koç University, Istanbul, Turkey | |
| 773 | 0 | |t Nanophotonics |g vol. 14, no. 16 (2025), p. 2723 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3238363813/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3238363813/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |