César Quilodrán-Casas
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View article: Data-driven Flood Prediction for Bangladesh at a 2-km Spatial Resolution Using ConvLSTM
Data-driven Flood Prediction for Bangladesh at a 2-km Spatial Resolution Using ConvLSTM Open
Flood prediction is a critical component of disaster preparedness, especially given the substantial economic and social consequences of flooding. As climate change intensifies the frequency and severity of extreme weather events, the need …
View article: DYffCast: Regional Precipitation Nowcasting Using IMERG Satellite Data. A case study over South America
DYffCast: Regional Precipitation Nowcasting Using IMERG Satellite Data. A case study over South America Open
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 precipitation is therefore becoming more critical for safeg…
View article: Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models Open
This study explores the application of diffusion models in the field of typhoons, predicting multiple ERA5 meteorological variables simultaneously from Digital Typhoon satellite images. The focus of this study is taken to be Taiwan, an are…
View article: Exploring unseen 3D scenarios of physics variables using machine learning-based synthetic data: An application to wave energy converters
Exploring unseen 3D scenarios of physics variables using machine learning-based synthetic data: An application to wave energy converters Open
View article: Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK
Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK Open
In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting. This study delves into this emerging trend, presenting our methodologies and o…
View article: T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency Open
While 3D visual self-supervised learning (vSSL) shows promising results in capturing visual representations, it overlooks the clinical knowledge from radiology reports. Meanwhile, 3D medical vision-language pre-training (MedVLP) remains un…
View article: Enhancing Microdroplet Image Analysis with Deep Learning
Enhancing Microdroplet Image Analysis with Deep Learning Open
Microfluidics is a highly interdisciplinary field where the integration of deep-learning models has the potential to streamline processes and increase precision and reliability. This study investigates the use of deep-learning methods for …
View article: Forecasting Tropical Cyclones with Cascaded Diffusion Models
Forecasting Tropical Cyclones with Cascaded Diffusion Models Open
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. This work leverages generative …
View article: Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review
Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review Open
Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to…
View article: Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias Open
The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from various language communities. Nevertheless, the main challenge st…
View article: A data-driven adversarial machine learning for 3D surrogates of unstructured computational fluid dynamic simulations
A data-driven adversarial machine learning for 3D surrogates of unstructured computational fluid dynamic simulations Open
This paper presents a general workflow to generate and improve the forecast of model surrogates of computational fluid dynamics simulations using deep learning, and most specifically adversarial training. This adversarial approach aims to …
View article: An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics Open
View article: Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling
Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling Open
Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we i…
View article: Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations
Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations Open
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. We…
View article: Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach
Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach Open
Improving surfactant-laden microdroplet size prediction using data-driven methods.
View article: An Efficient Digital Twin Based on Machine Learning Svd Autoencoder and Generalised Latent Assimilation for Nuclear Reactor Physics
An Efficient Digital Twin Based on Machine Learning Svd Autoencoder and Generalised Latent Assimilation for Nuclear Reactor Physics Open
View article: Reduced Order Surrogate Modelling and Latent Assimilation for Dynamical Systems
Reduced Order Surrogate Modelling and Latent Assimilation for Dynamical Systems Open
View article: Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic Open
View article: Digital twins based on bidirectional LSTM and GAN for modelling the\n COVID-19 pandemic
Digital twins based on bidirectional LSTM and GAN for modelling the\n COVID-19 pandemic Open
The outbreak of the coronavirus disease 2019 (COVID-19) has now spread\nthroughout the globe infecting over 150 million people and causing the death of\nover 3.2 million people. Thus, there is an urgent need to study the dynamics of\nepide…