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. 2307.04010
  4. Cited By
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater
  Numerical Modelling

Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling

8 July 2023
M. L. Taccari
O. Ovadia
He Wang
Adar Kahana
Xiaohui Chen
P. Jimack
ArXiv (abs)PDFHTML

Papers citing "Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling"

1 / 1 papers shown
Title
PIC2O-Sim: A Physics-Inspired Causality-Aware Dynamic Convolutional
  Neural Operator for Ultra-Fast Photonic Device FDTD Simulation
PIC2O-Sim: A Physics-Inspired Causality-Aware Dynamic Convolutional Neural Operator for Ultra-Fast Photonic Device FDTD Simulation
Pingchuan Ma
Haoyu Yang
Zhengqi Gao
Duane S. Boning
Jiaqi Gu
215
4
0
24 Jun 2024
1