Search Results for author:
Found 8 papers, 2 papers with code
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
In this work, we propose a downscaling method based on the Fourier neural operator.
Hard-Constrained Deep Learning for Climate Downscaling
In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics.
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network
An impact of climate change is the increase in frequency and intensity of extreme precipitation events.
A modular framework for extreme weather generation
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change.
Semantic Segmentation of Seismic Images
We compare our approach with two well-known deep neural network topologies: Fully Convolutional Network and U-Net.
Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation
In this work, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation.
