Nature Search

Implementing the analogous neural network using chaotic strange attractors

Bahadır Utku Kesgin and Uğur Teğin propose using a Lorenz attractor as a nonlinear transfer function for neural network nodes. They design a power-efficient electrical circuit and use them for regression and classification test tasks.

Bahadır Utku Kesgin
Uğur Teğin
ResearchOpen Access15 Jul 2024
Communications Engineering

Volume: 3, P: 1-8
Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network

Uğur Teğin
Niyazi Ulaş Dinç
Demetri Psaltis
Research13 May 2021
Nature Machine Intelligence

Volume: 3, P: 387-391
Cascaded Raman scattering based high power octave-spanning supercontinuum generation in graded-index multimode fibers

Uğur Teğin
Bülend Ortaç
ResearchOpen Access20 Aug 2018
Scientific Reports

Volume: 8, P: 1-7
Scalable optical learning operator

Optical computing promises high-speed computations but presents challenges in nonlinear information processing. This Article demonstrates a scalable and energy-efficient nonlinear optical-computing framework that can perform machine learning tasks.

Uğur Teğin
Mustafa Yıldırım
Demetri Psaltis
Research20 Aug 2021
Nature Computational Science

Volume: 1, P: 542-549
Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media

Machine learning has become popular in solving complex optical problems such as recovering the input phase and amplitude for a specific pattern or image measured through a scattering medium. In a more challenging application, Rahmani et al. consider the problem of also producing desired outputs for such a nonlinear system when only some intensity-only measurements of example outputs are available. They develop a neural network approach that can ensure the transmission of images through a highly nonlinear system—a multimode fibre—with a 90% fidelity.

Babak Rahmani
Damien Loterie
Christophe Moser
Research15 Jul 2020
Nature Machine Intelligence

Volume: 2, P: 403-410
Genetically programmable optical random neural networks

Optics offers highly parallelized and energy-efficient computations, making it suitable for answering the ever-increasing demands of artificial intelligence systems. Here, the authors demonstrate a programmable optical random neural network capable of performing classification tasks simply by optimizing the angular orientation of a scattering medium.

Bora Çarpınlıoğlu
Uğur Teğin
ResearchOpen Access25 Aug 2025
Communications Physics

Volume: 8, P: 1-8
Real-time observation of optical rogue waves in spatiotemporally mode-locked fiber lasers

Optical rogue waves emerge as the optical analogue to oceanic rogue waves, namely as a temporally rare fluctuations characterized by exceptionally high intensities. The authors observe the emergence and the real-time dynamics of such intensity bursts undergoing nonlinear spatial transformation in spatiotemporally mode-locked fiber lasers.

Uğur Teğin
Peng Wang
Lihong V. Wang
ResearchOpen Access05 Apr 2023
Communications Physics

Volume: 6, P: 1-8

Search

Filter By:

Implementing the analogous neural network using chaotic strange attractors

Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network

Cascaded Raman scattering based high power octave-spanning supercontinuum generation in graded-index multimode fibers

Scalable optical learning operator

Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media

Genetically programmable optical random neural networks

Real-time observation of optical rogue waves in spatiotemporally mode-locked fiber lasers

Search

Quick links