48 citations · 247 across the 37 of their papers we have counts for
20 papers · 1 filter
A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
Wenbo Yu, Anirbit Ghosh, Tobias Sebastian Finn +3
Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task mad…
Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs
Che Liu, Cheng Ouyang, Zhongwei Wan +3
Recent advances in multimodal ECG representation learning center on aligning ECG signals with paired free-text reports. However, suboptimal alignment persists due to the complexity…
Machine learning for modelling unstructured grid data in computational physics: a review
Sibo Cheng, Marc Bocquet, Weiping Ding +20
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for co…
DYffCast: Regional Precipitation Nowcasting Using IMERG Satellite Data. A case study over South America
Daniel Seal, Rossella Arcucci, Salva Rühling-Cachay +1
Climate change is increasing the frequency of extreme precipitation events, making weather disasters such as flooding and landslides more likely. The ability to accurately nowcast…
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
Bo Pang, Sibo Cheng, Yuhan Huang +5
Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexi…
Deep learning surrogate models of JULES-INFERNO for wildfire prediction on a global scale
Sibo Cheng, Hector Chassagnon, Matthew Kasoar +2
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 emis…