ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2210.16215
  4. Cited By
Physics-Informed Convolutional Neural Networks for Corruption Removal on
  Dynamical Systems

Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems

28 October 2022
Daniel Kelshaw
Luca Magri
ArXivPDFHTML

Papers citing "Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems"

2 / 2 papers shown
Title
Physics-informed Convolutional Neural Networks for Temperature Field
  Prediction of Heat Source Layout without Labeled Data
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
Xiaoyu Zhao
Zhiqiang Gong
Yunyang Zhang
Wen Yao
Xiaoqian Chen
OOD
AI4CE
77
91
0
26 Sep 2021
Real-Time Single Image and Video Super-Resolution Using an Efficient
  Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi
Jose Caballero
Ferenc Huszár
J. Totz
Andrew P. Aitken
Rob Bishop
Daniel Rueckert
Zehan Wang
SupR
198
5,176
0
16 Sep 2016
1