2 citations · 2 across the 5 of their papers we have counts for
5 papers
TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
Tobia Boschi, Andrea Loreti, Nicola C. Amorisco +13
We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous d…
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
Cécile Rousseau, Samuel Jackson, Rodrigo H. Ordonez-Hurtado +13
Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors…
Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation
Cécile Rousseau, Tobia Boschi, Giandomenico Cornacchia +3
SDForger is a flexible and efficient framework for generating high-quality multivariate time series using LLMs. Leveraging a compact data representation, SDForger provides syntheti…
Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights
Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado +3
This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks to address the compl…
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spaces
Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado +2
This paper introduces a novel methodology for Feature Selection for Functional Classification, FSFC, that addresses the challenge of jointly performing feature selection and classi…