68 citations · 730 across the 54 of their papers we have counts for
6 papers · 1 filter
Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network
Fan Fan, Yilei Shi, Tobias Guggemos +1
Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) h…
Physically consistent and uncertainty-aware learning of spatiotemporal dynamics
Qingsong Xu, Jonathan L Bamber, Nils Thuerey +5
Accurate long-term forecasting of spatiotemporal dynamics remains a fundamental challenge across scientific and engineering domains. Existing machine learning methods often neglect…
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Shan Zhao, Zhaiyu Chen, Zhitong Xiong +3
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Net…
Physics-embedded Fourier Neural Network for Partial Differential Equations
Qingsong Xu, Nils Thuerey, Yilei Shi +3
We consider solving complex spatiotemporal dynamical systems governed by partial differential equations (PDEs) using frequency domain-based discrete learning approaches, such as Fo…
Large-scale flood modeling and forecasting with FloodCast
Qingsong Xu, Yilei Shi, Jonathan Bamber +2
Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to a…
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Qingsong Xu, Yilei Shi, Jonathan Bamber +3
Accurate hydrological understanding and water cycle prediction are crucial for addressing scientific and societal challenges associated with the management of water resources, part…