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Found 8 papers, 6 papers with code
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models
This study explores the application of diffusion models in the field of typhoons, predicting multiple ERA5 meteorological variables simultaneously from Digital Typhoon satellite images.
Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK
In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting.
Forecasting Tropical Cyclones with Cascaded Diffusion Models
As tropical cyclones become more intense due to climate change, the rise of Al-based modelling provides a more affordable and accessible approach compared to traditional methods based on mathematical models.
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Med-UniC reaches superior performance across 5 medical image tasks and 10 datasets encompassing over 30 diseases, offering a versatile framework for unifying multi-modal medical data within diverse linguistic communities.
Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations
Starting from the Kaya identity, we used a Neural ODE model to predict the evolution of several indicators related to carbon emissions, on a country-level: population, GDP per capita, energy intensity of GDP, carbon intensity of energy.
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
Our two-step method integrates a Principal Components Analysis (PCA) based adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM) networks.
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
Here we introduce two digital twins of a SEIRS model applied to an idealised town.
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
This adversarially trained LSTM-based approach is used on the ROM in order to produce faster forecasts of the air pollution tracer.
