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Found 7 papers, 5 papers with code
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.
