Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks-Reference-Cited by-同舟云学术

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Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks

Published:2024 Issue: Volume:12 Page:55218-55224
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Cheng Liang¹^ORCID,Singh Prashant²,Ferranti Francesco³^ORCID

Affiliation:

1. Department of Information Technology, Uppsala University, Uppsala, Sweden

2. Department of Information Technology, Science for Life Laboratory, Division of Scientific Computing, Uppsala University, Uppsala, Sweden

3. Department of Applied Physics and Photonics, Brussels Photonics and Flanders Make, Vrije Universiteit Brussel, Brussels, Belgium

Funder

National Academic Infrastructure for Supercomputing in Sweden

Swedish Research Council

Methusalem and Hercules Foundations

Research Council (OZR), Vrije Universiteit Brussel

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10486893.pdf?arnumber=10486893

Reference18 articles.

1. Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

2. Nanophotonic particle simulation and inverse design using artificial neural networks

3. Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures

4. Deep Convolutional Mixture Density Network for Inverse Design of Layered Photonic Structures

5. Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures

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