Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
Data-driven and knowledge-guided denoising diffusion model for flood forecasting
Data-driven models have been successfully applied in hydrological fields such as flood
forecasting. However, limitations to the solutions to scientific problems still exist in this field …
forecasting. However, limitations to the solutions to scientific problems still exist in this field …
A survey on diffusion models for time series and spatio-temporal data
The study of time series data is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …
Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models
Abstract The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution
regional reanalysis dataset for the European domain. In recent years, it has shown …
regional reanalysis dataset for the European domain. In recent years, it has shown …
Generative diffusion for regional surrogate models from sea‐ice simulations
We introduce deep generative diffusion for multivariate and regional surrogate modeling
learned from sea‐ice simulations. Given initial conditions and atmospheric forcings, the …
learned from sea‐ice simulations. Given initial conditions and atmospheric forcings, the …
Non-gaussian hydraulic conductivity and potential contaminant source identification: A comparison of two advanced DLPM-based inversion framework
Accurate identification of hydraulic conductivity fields (K) and contaminant source
parameters is imperative for the enhanced assessment and effective remediation of polluted …
parameters is imperative for the enhanced assessment and effective remediation of polluted …
Integration of DDPM and ILUES for simultaneous identification of contaminant source parameters and non‐Gaussian channelized hydraulic conductivity field
Identifying highly channelized hydraulic conductivity fields and contaminant source
parameters remains a challenging task, primarily due to the non‐Gaussian nature and high …
parameters remains a challenging task, primarily due to the non‐Gaussian nature and high …
Weather Prediction with Diffusion Guided by Realistic Forecast Processes
Weather forecasting remains a crucial yet challenging domain, where recently developed
models based on deep learning (DL) have approached the performance of traditional …
models based on deep learning (DL) have approached the performance of traditional …
[HTML][HTML] LLMDiff: Diffusion Model Using Frozen LLM Transformers for Precipitation Nowcasting
L She, C Zhang, X Man, J Shao - Sensors, 2024 - mdpi.com
Precipitation nowcasting, which involves the short-term, high-resolution prediction of rainfall,
plays a crucial role in various real-world applications. In recent years, researchers have …
plays a crucial role in various real-world applications. In recent years, researchers have …
A generative approach to person reidentification
A Asperti, S Fiorilla, L Orsini - Sensors, 2024 - mdpi.com
Person Re-identification is the task of recognizing comparable subjects across a network of
nonoverlapping cameras. This is typically achieved by extracting from the source image a …
nonoverlapping cameras. This is typically achieved by extracting from the source image a …