WE3.R9.5
PHYSICS-GUIDED SPATIOTEMPORAL NEURAL MODELS FOR FUEL DENSITY PREDICTION
Tolga Caglar, Jaynil Jaiswal, Saqib Azim, Yudhir Gala, Mai Nguyen, Ilkay Altintas, University of California, San Diego, United States
Session:
WE3.R9: Physics-Informed Machine Learning in Remote Sensing (3/4) Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Wed, 12 Aug, 14:45 - 15:00
Session Co-Chairs:
Davide De Santis, and Grigorios Tsagkatakis,
Presentation
Discussion
Resources
No resources available.
Session WE3.R9
WE3.R9.1: QUATERNION INVERSE MAPPING IN POLARIMETRIC SYNTHETIC APERTURE RADAR FOR LAND CLASSIFICATION
Hiroki Onishi, The University of Tokyo, Japan; Gunjan JOSHI, Helmholtz-Zentrum Dresden-Rossendorf, Germany; Ryo Natsuaki, Akira Hirose, The University of Tokyo, Japan
WE3.R9.2: 30m-Resolution Leaf Area Index Retrieval by Coupling Physical Model and Spatiotemporal Deep Learning
Dechao Zhai, Naijie Peng, Qunchao He, Zhicheng Huang, Huazhong Ren, Wenjie Fan, Peking University, China
WE3.R9.3: RAPID NEURAL SURROGATE MODELING OF EARTHQUAKE-INDUCED MUDSLIDES
Selma Emekci, Pioneer High School, United States; Ünal Göktaş, Texas A&M University, United States
WE3.R9.4: PHYSICS-CONDITIONED SYNTHESIS OF INTERNAL ICE-LAYER THICKNESS FOR INCOMPLETE LAYER TRACES
Zesheng Liu, Maryam Rahnemoonfar, Lehigh University, United States
WE3.R9.5: PHYSICS-GUIDED SPATIOTEMPORAL NEURAL MODELS FOR FUEL DENSITY PREDICTION
Tolga Caglar, Jaynil Jaiswal, Saqib Azim, Yudhir Gala, Mai Nguyen, Ilkay Altintas, University of California, San Diego, United States
Contacts