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Migrating Knowledge between Physical Scenarios based on Artificial
  Neural Networks
v1v2 (latest)

Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks

27 August 2018
Yurui Qu
Li Jing
Yichen Shen
Min Qiu
Marin Soljacic
    MedImAI4CE
ArXiv (abs)PDFHTML

Papers citing "Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks"

10 / 10 papers shown
Interpretable inverse design of optical multilayer thin films based on extended neural adjoint and regression activation mapping
Interpretable inverse design of optical multilayer thin films based on extended neural adjoint and regression activation mapping
Sungjun Kim
Jungho Kim
186
0
0
10 Jul 2025
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
Yuheng Chen
Alexander Montes McNeil
Taehyuk Park
Blake A. Wilson
Vaishnavi Iyer
...
Vladimir M. Shalaev
Michael Moebius
Wenshan Cai
Yongmin Liu
A. Boltasseva
284
9
0
24 Jun 2025
OL-Transformer: A Fast and Universal Surrogate Simulator for Optical
  Multilayer Thin Film Structures
OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures
Taigao Ma
Haozhu Wang
L. J. Guo
AI4CE
215
2
0
19 May 2023
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution
  Imaging
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution ImagingACS Applied Materials and Interfaces (ACS Appl. Mater. Interfaces), 2023
Jin Zhao
Huang Zhang
Ming-Zhe Chong
Yuewan Zhang
Zi‐Wen Zhang
Zong-Kun Zhang
C. Du
Pu‐Kun Liu
SupR
67
0
0
02 May 2023
Surrogate- and invariance-boosted contrastive learning for data-scarce
  applications in science
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Charlotte Loh
T. Christensen
Rumen Dangovski
Samuel Kim
Marin Soljacic
241
25
0
15 Oct 2021
Inverse Design of Grating Couplers Using the Policy Gradient Method from
  Reinforcement Learning
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
S. Hooten
R. Beausoleil
T. Van Vaerenbergh
390
30
0
30 Jun 2021
Deep neural networks for the evaluation and design of photonic devices
Deep neural networks for the evaluation and design of photonic devices
Jiaqi Jiang
Ming-Keh Chen
Jonathan A. Fan
302
492
0
30 Jun 2020
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with
  Reinforcement Learning
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement LearningPhysical Chemistry, Chemical Physics - PCCP (PCCP), 2019
Wenqi Wei
Ling Liu
J. Rho
137
55
0
28 Oct 2019
Deep Learning Reveals Underlying Physics of Light-matter Interactions in
  Nanophotonic Devices
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic DevicesAdvanced Theory and Simulations (ATS), 2019
Yashar Kiarashinejad
Sajjad Abdollahramezani
M. Zandehshahvar
Omid Hemmatyar
A. Adibi
179
97
0
07 May 2019
Deep learning approach based on dimensionality reduction for designing
  electromagnetic nanostructures
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructuresnpj Computational Materials (npj Comput. Mater.), 2019
Yashar Kiarashinejad
Sajjad Abdollahramezani
A. Adibi
210
199
0
11 Feb 2019
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