Blogs

(Special Issue) – Transport-Based and Flow Map Learning Generative Models: Advances and Perspectives

Guest Editor Emanuel di Nardo,  Thierry Bouwmans, Chieh-Hsin Lai, Francesco Camastra Co-Guest Editor Angelo Casolaro Journal Information (MDPI) Call for Paper (CFP): In recent years, the breakthrough of generative models‘ performance on disparate applications has raised a huge interest in real-world challenges. In particular, the evolution of Deep Generative Models (DGMs) is crucial since they […]

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(Workshop) – Data-Driven Decision-Making: Uncertainty and Reliable Decision-Making by Generative AI – IJCNN 2026

Organizers Angelo Casolaro, Emanuel di Nardo, Angelo Ciaramella, Thierry Bouwmans, Inhsan Ullah Conference International Joint Conference in Neural Networks 2026 (IJCNN26)  Abstract Generative AI (GenAI) is reshaping how complex data are modeled, interpreted, and applied across science, engineering, and industry. This workshop explores the predictive power and practical value of GenAI, with a strong focus […]

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Deep Learning for Regular Raster Spatio-Temporal Prediction: An Overview

Authors Vincenzo Capone, Angelo Casolaro, Francesco Camastra Journal Information (MDPI) Abstract The raster is the most common type of spatio-temporal data, and it can be either regularly or irregularly spaced. Spatio-temporal prediction on regular raster data is crucial for modelling and understanding dynamics in disparate realms, such as environment, traffic, astronomy, remote sensing, gaming and […]

Journal Papers

Flow Matching for Simulation-Based Inference: Design Choices and Implications

Authors Massimiliano Giordano Orsini, Alessio Ferone, Laura Inno, Angelo Casolaro, Antonio Maratea Journal Electronics (MDPI) Abstract Inverse problems are ubiquitous across many scientific fields, and involve the determination of the causes or parameters of a system from observations of its effects or outputs. These problems have been deeply studied through the use of simulated data, […]

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Spatio-temporal prediction using graph neural networks: A survey

Authors Vincenzo Capone, Angelo Casolaro, Francesco Camastra Journal Neurocomputing (Elsevier) Abstract The analysis of spatial time series is increasingly relevant as spatio-temporal data are becoming widespread due to the ever-growing diffusion of data acquisition devices. Spatio-temporal prediction is crucial for grasping insights on spatio-temporal dynamics in diverse domains. In many cases, spatio-temporal data can be […]

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(ISCRA project Class C) – Continuous super-resolution of primary air pollutants using implicit diffusion models

Research Group Angelo Casolaro, Francesco Camastra, Thierry Bouwmans, Vincenzo Capone Organization CINECA   Program Italian SuperComputing Resource Allocation – ISCRA   Executive Summary: Air pollution remains a persistent environmental and public health challenge in developed Western countries, with primary pollutants such as fine particulate matter, nitrogen dioxide, ozone, and sulphur dioxide posing serious risks. Accurate, […]

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Flow Matching Posterior Estimation for Simulation-based Atmospheric Retrieval of Exoplanets

Authors Massimiliano Giordano Orsini, Alessio Ferone, Laura Inno, Angelo Casolaro, Antonio Maratea Journal IEE Access (IEEE) Abstract The characterization of exoplanetary atmospheres allows a deeper understanding of planetary formation, evolution, and habitability through atmospheric retrieval, which consists in inferring various properties of exoplanetary atmospheres given their spectroscopic observations. Traditional atmospheric retrieval methods based on Bayesian […]

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Environmental Spatiotemporal prediction with a Conditioned Diffusion-based Graph Attention model

Authors Angelo Casolaro, Vincenzo Capone, Massimiliano Giordano Orsini, Francesco Camastra Conference International Joint Conference on Neural Networks (IJCNN) 2025 Abstract Environmental spatiotemporal prediction is crucial for air quality management, where accurate prediction of primary air pollutants is essential for public health and policymaking. This paper introduces the Conditioned Diffusion-based Graph Attention (CDGA) model, a novel […]

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Predicting ground-level nitrogen dioxide concentrations using the BaYesian attention-based deep neural network

Authors Angelo Casolaro, Vincenzo Capone, Francesco Camastra Journal Ecological Informatics (Elsevier) Abstract Nitrogen dioxide pollution is an ongoing and growing environmental issue that affects human health in developed Western countries. This study introduced a Bayesian attention-based deep neural network model for predicting ground-level nitrogen dioxide concentrations. The proposed model integrates the principles of the Bayesian […]

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A data-driven approach for extracting exoplanetary atmospheric features

Authors Massimiliano Giordano Orsini, Alessio Ferone, Laura Inno, Paolo Giacobbe, Antonio Maratea, Angelo Ciaramella, Aldo Stefano Bonomo, Alessandra Rotundi Journal Astronomy and Computing (Elsevier) Abstract Ground-based high-resolution transmission spectroscopy has become a critical tool for probing the chemical compositions of transiting exoplanetary atmospheres. A well-known challenge in this scope lies in the detrending process, which […]

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