TU3.R9.3
PHYSICS-INFORMED NEURAL NETWORKS FOR HYDROLOGICALLY DRIVEN SLOW-MOVING LANDSLIDE DISPLACEMENT MODELING USING INSAR TIME SERIES
Zhe Zhang, Xiaochuan Tang, Xuanmei Fan, Lei Zhang, Yifan Feng, Zhenlei Wei, Qing Pan, Chengdu University of Technology, China; Daniel Kibirige, University of Cape Town, South Africa; Filippo Catani, University of Padova, Italy
Session:
TU3.R9: Physics-Informed Machine Learning in Remote Sensing (1/4) Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Tue, 11 Aug, 14:15 - 14:30
Session Co-Chairs:
Davide De Santis, and Grigorios Tsagkatakis,
Presentation
Discussion
Resources
No resources available.
Session TU3.R9
TU3.R9.1: Physics-Integrated Deep Learning for Electromagnetic Inversion with Numerical Gradients: An LWD Case Study
Zhongrui Wang, University of Houston, United States; Hanming Wang, Chevron Technical Center, United States; Xiaotian Wang, Jiarui Lu, Jianfeng Zheng, University of Houston, United States
TU3.R9.2: Physics-Guided VAE with Refinement Networks and Extended Latent Space for Improved LAI Estimation
Kevin De Sousa, Jordi Inglada, Centre d’Etudes Spatiales de la Biosphère, France
TU3.R9.3: PHYSICS-INFORMED NEURAL NETWORKS FOR HYDROLOGICALLY DRIVEN SLOW-MOVING LANDSLIDE DISPLACEMENT MODELING USING INSAR TIME SERIES
Zhe Zhang, Xiaochuan Tang, Xuanmei Fan, Lei Zhang, Yifan Feng, Zhenlei Wei, Qing Pan, Chengdu University of Technology, China; Daniel Kibirige, University of Cape Town, South Africa; Filippo Catani, University of Padova, Italy
TU3.R9.4: PF-TRANS: PHYSICS-EMBEDDED FREQUENCY-AWARE TRANSFORMER FOR SPECTRAL RECONSTRUCTION
Yuzhe Gui, Tianzhu Liu, Yanfeng Gu, Xian Li, Harbin Institute of Technology, China
TU3.R9.5: PICSRL: PHYSICS-INFORMED CONTEXTUAL SPECTRAL REINFORCEMENT LEARNING
Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin Alamdari, Florida State University, United States
Contacts