ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2304.03689
  4. Cited By
EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving
  Navier-Stokes Equations

EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations

7 April 2023
Ayoub Farkane
Mounir Ghogho
M. Oudani
M. Boutayeb
    PINN
ArXiv (abs)PDFHTML

Papers citing "EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations"

3 / 3 papers shown
Title
Can Real-to-Sim Approaches Capture Dynamic Fabric Behavior for Robotic Fabric Manipulation?
Can Real-to-Sim Approaches Capture Dynamic Fabric Behavior for Robotic Fabric Manipulation?
Yingdong Ru
Lipeng Zhuang
Zhuo He
Florent P. Audonnet
Gerardo Aragon-Caramasa
AI4CE
310
1
0
20 Mar 2025
AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks
  for Carbon-Efficient Climate Modeling
AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling
Siddhant Dutta
Nouhaila Innan
Sadok Ben Yahia
Muhammad Shafique
PINN
192
6
0
03 Sep 2024
VS-PINN: A fast and efficient training of physics-informed neural
  networks using variable-scaling methods for solving PDEs with stiff behavior
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
Seungchan Ko
Sang Hyeon Park
248
15
0
10 Jun 2024
1