Zobrazeno 1 - 10
of 204
pro vyhledávání: '"Cheng Sibo"'
Autor:
Pang, Bo, Cheng, Sibo, Huang, Yuhan, Jin, Yufang, Guo, Yike, Prentice, I. Colin, Harrison, Sandy P., Arcucci, Rossella
Publikováno v:
Computers & Geosciences, Volume 195, 2025, 105783
Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in pre
Externí odkaz:
http://arxiv.org/abs/2412.01400
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 2024 Dec 1
Despite the success of various methods in addressing the issue of spatial reconstruction of dynamical systems with sparse observations, spatio-temporal prediction for sparse fields remains a challenge. Existing Kriging-based frameworks for spatio-tem
Externí odkaz:
http://arxiv.org/abs/2409.00458
Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observatio
Externí odkaz:
http://arxiv.org/abs/2409.00244
Global wildfire models play a crucial role in anticipating and responding to changing wildfire regimes. JULES-INFERNO is a global vegetation and fire model simulating wildfire emissions and area burnt on a global scale. However, because of the high d
Externí odkaz:
http://arxiv.org/abs/2409.00237
Diffusion models have gained attention for their ability to represent complex distributions and incorporate uncertainty, making them ideal for robust predictions in the presence of noisy or incomplete data. In this study, we develop and enhance score
Externí odkaz:
http://arxiv.org/abs/2409.00230
Autor:
Bocquet, Marc, Farchi, Alban, Finn, Tobias S., Durand, Charlotte, Cheng, Sibo, Chen, Yumeng, Pasmans, Ivo, Carrassi, Alberto
We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual
Externí odkaz:
http://arxiv.org/abs/2408.04739
Wildfires have significant impacts on global vegetation, wildlife, and humans. They destroy plant communities and wildlife habitats and contribute to increased emissions of carbon dioxide, nitrogen oxides, methane, and other pollutants. The predictio
Externí odkaz:
http://arxiv.org/abs/2405.01607
Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change. Traditional CNN-based wildfire prediction models struggle with handling missing oceanic data and addressing the long-range dependencies across distan
Externí odkaz:
http://arxiv.org/abs/2402.07152
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 2024 Feb 15;420:116758
Physics-constrained neural networks are commonly employed to enhance prediction robustness compared to purely data-driven models, achieved through the inclusion of physical constraint losses during the model training process. However, one of the majo
Externí odkaz:
http://arxiv.org/abs/2402.02031
Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large pre-trained models to learn versatile medical vision representations.
Externí odkaz:
http://arxiv.org/abs/2401.01179