scholarly journals Machine-learning-assisted inverse design of scattering enhanced metasurface

2022 ◽  
Author(s):  
Hai Lin ◽  
junjie hou ◽  
jing jin ◽  
yumei wang ◽  
Rongxin Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inverse Design

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

scholarly journals Predictive and generative machine learning models for photonic crystals

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4183-4192 ◽  
Author(s):  
Thomas Christensen ◽  
Charlotte Loh ◽  
Stjepan Picek ◽  
Domagoj Jakobović ◽  
Li Jing ◽  
...  
Keyword(s):  
Machine Learning ◽  
Photonic Crystals ◽  
Optimization Techniques ◽  
Inverse Design ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Test Bed ◽  
Data Set ◽  
Wide Range

AbstractThe prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences.

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