Machine learning frameworks for the inverse design of highly complicated multi-functional metasystems

2020 ◽  
Author(s):  
Wenshan Cai
Keyword(s):  
Machine Learning ◽  
Inverse Design ◽  
Learning Frameworks

scholarly journals Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3654
Author(s):  
Nastaran Gholizadeh ◽  
Petr Musilek
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Learning Algorithm ◽  
Single Point ◽  
Distributed Learning ◽  
Large Data ◽  
Multi Agent Systems ◽  
Power Quality Monitoring ◽  
Multi Agent ◽  
Learning Frameworks

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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 Relay: a new IR for machine learning frameworks

2018 ◽  
Author(s):  
Jared Roesch ◽  
Steven Lyubomirsky ◽  
Logan Weber ◽  
Josh Pollock ◽  
Marisa Kirisame ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Frameworks

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.

Experience report: investigating bug fixes in machine learning frameworks/libraries

2021 ◽  
Vol 15 (6) ◽  
Author(s):  
Xiaobing Sun ◽  
Tianchi Zhou ◽  
Rongcun Wang ◽  
Yucong Duan ◽  
Lili Bo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experience Report ◽  
Bug Fixes ◽  
Learning Frameworks

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
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