scholarly journals Intelligent gain flattening in wavelength and space domain for FMF Raman amplification by machine learning based inverse design

2020 ◽  
Vol 28 (8) ◽  
pp. 11911
Author(s):  
Yufeng Chen ◽  
Jiangbing Du ◽  
Yuting Huang ◽  
Ke Xu ◽  
Zuyuan He
Keyword(s):  
Machine Learning ◽  
Inverse Design ◽  
Raman Amplification ◽  
Space Domain ◽  
Gain Flattening

scholarly journals Machine Learning Assisted Inverse Design for Ultrafine, Dynamic and Arbitrary Gain Spectrum Shaping of Raman Amplification

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 260
Author(s):  
Yuting Huang ◽  
Jiangbing Du ◽  
Yufeng Chen ◽  
Ke Xu ◽  
Zuyuan He
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Noise Figure ◽  
Dynamic Change ◽  
Low Noise ◽  
Gain Spectrum ◽  
Inverse Design ◽  
Raman Amplification ◽  
Spectrum Shaping ◽  
Spectrum Optimization

Distributed Raman amplifier (DRA) has been widely studied in recent decades because of its low noise figure and flexible gain. In this paper, we present a novel scheme of DRA with broadband amplified spontaneous emission(ASE) source as pump instead of discrete pump lasers. The broadband pump is optimized by machine learning based inverse design and shaped by programmable waveshaper, so as to realize the ultrafine, dynamic and arbitrary gain spectrum shaping of Raman amplification. For the target of flat gain spectrum, the maximum gain flatness of 0.1086 dB is realized based on the simulation results. For the target of arbitrary gain spectrum, we demonstrate four gain profiles with maximum root mean square error (RMSE) of 0.074 dB. To further measure the performance of arbitrary gain spectrum optimization, the probability density functions (PDF) of RMSE and Errormax are presented. Meanwhile, the numeral relationship between the bands of broadband pump and signal is also explored. Furthermore, this work has great application potential to compensate the gain distortion or dynamic change caused by other devices in communication systems.

scholarly journals Tackling Photonic Inverse Design with Machine Learning

2021 ◽  
pp. 2002923
Author(s):  
Zhaocheng Liu ◽  
Dayu Zhu ◽  
Lakshmi Raju ◽  
Wenshan Cai
Keyword(s):  
Machine Learning ◽  
Inverse Design

scholarly journals Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes

Nanophotonics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 385-392
Author(s):  
Joeri Lenaerts ◽  
Hannah Pinson ◽  
Vincent Ginis
Keyword(s):  
Machine Learning ◽  
Optimization Procedure ◽  
Linear Mapping ◽  
Inverse Design ◽  
Design Parameters ◽  
Second Step ◽  
Input Parameters ◽  
Novel Variant ◽  
Resonant Systems ◽  
The Fourier Transform

AbstractMachine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system’s features. The second step starts from the desired features, e.g. a transmission spectrum, and propagates back through the trained network to find the optimal input parameters. For resonant systems, this second step corresponds to a gradient descent in a highly oscillatory loss landscape. As a result, the algorithm often converges into a local minimum. We significantly improve this method’s efficiency by adding the Fourier transform of the desired spectrum to the optimization procedure. We demonstrate our method by retrieving the optimal design parameters for desired transmission and reflection spectra of Fabry–Pérot resonators and Bragg reflectors, two canonical optical components whose functionality is based on wave interference. Our results can be extended to the optimization of more complex nanophotonic components interacting with structured incident fields.

scholarly journals Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design

2018 ◽  
Author(s):  
Jatin Kumar ◽  
Qianxiao Li ◽  
Karen Y.T. Tang ◽  
Tonio Buonassisi ◽  
Anibal L. Gonzalez-Oyarce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Point ◽  
Disordered Systems ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inverse Design ◽  
Gradient Boosting ◽  
Multiple Length Scales ◽  
The Status

<div><div><div><p>Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24– 90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.</p></div></div></div>

scholarly journals Machine learning aided inverse design for few-mode fiber weak-coupling optimization

2020 ◽  
Vol 28 (15) ◽  
pp. 21668
Author(s):  
Zhiqin He ◽  
Jiangbing Du ◽  
Xinyi Chen ◽  
Weihong Shen ◽  
Yuting Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weak Coupling ◽  
Inverse Design

Physics based Machine Learning for inverse design of metasurfaces

2021 ◽  
Author(s):  
Sulagna Sarkar, PhD student ◽  
Anqi Ji ◽  
Zachary Jermain ◽  
Robert Lipton ◽  
Mark L Brongersma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inverse Design

Inverse Design of Inflatable Soft Membranes Through Machine Learning

2022 ◽  
pp. 2111610
Author(s):  
Antonio Elia Forte ◽  
Paul Z. Hanakata ◽  
Lishuai Jin ◽  
Emilia Zari ◽  
Ahmad Zareei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inverse Design
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