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:
Fairchild
Presentation Time:
Wednesday, 12 August, 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
Resources
No resources available.