DATAHYKING Marie Curie EU Doctoral Network (number 101072546)
Investigators: Gabriella Puppo (local coordinator), Ilaria Ciaramaglia, Martin Fleurial, Jiehong Liu, Tommaso Tenna, Giuseppe Visconti
Website
The project aims at training a new generation of modeling and simulation experts to develop virtual experimentation tools and workflows that can reliably and efficiently exploit the potential of mathematical modeling and simulation of interacting particle systems. To this end, data-driven simulation framework for kinetic models of interacting particle systems, and a common methodology for these future modeling and simulation experts, will be created.
MUR - PRIN 2022 Project (number 2022238YY5)
"Optimal control problems: analysis, approximation"
Investigators: Elisabetta Carlini (local coordinator), Alessandro Alla
MUR - PRIN 2022 Project (number 2022JH87B4)
“High order structure-preserving semi-implicit schemes for hyperbolic equations”
Investigators: Gabriella Puppo (local coordinator), Alessandra Spilimbergo, Giuseppe Visconti
MUR - PRIN 2022 PNRR Project (number P2022JC95T)
“Data-driven discovery and control of multiscale interacting artificial agent systems”
Investigators: Giuseppe Visconti (local coordinator), Alessandro Alla, Stephan Gerster, Agnese Pacifico
Website
The main goal of the project consists in developing sustainable and flexible next-generation frameworks for data-driven modelling, optimization, and simulation of multi-scale interacting agent systems of utmost importance in industrial applications and socio-economic life. As scientific aim, we investigate several approaches relying on learning-based mathematical methods to build and control physical data-driven models. The proposal is timely since learning-based methods have recently attracted the attention of the scientific community to fully exploit HPC hardware and the abundance of data, demanding new unifying concepts to address grand challenges.
Sapienza Ateneo Project 2023
“Modeling, numerical treatment of hyperbolic equations and optimal control problems”
Investigators: Giuseppe Visconti (coordinator), Alessandro Alla, Simone Cacace, Elisabetta Carlini, Silvia Noschese, Gabriella Puppo, Davide Torlo
This research proposal contributes to advancing numerical methods for Hamilton-Jacobi, Fokker-Planck equations and hyperbolic systems of conservation laws. Furthermore, the project is focused on the analysis of complex networks and explores applications to problems arising in traffic flow, epidemiology, data clustering and optimal control. The findings of the project have the potential to improve current numerical methods, enhancing modeling techniques, and enabling more effective control strategies in domains such as transportation engineering, public health, and data analysis.
Sapienza Ateneo Project 2024
“Advanced Computational Methods for Real-World Applications: Data-Driven Models, Hyperbolic Equations and Optimal Control”
Investigators: Giuseppe Visconti (coordinator), Simone Cacace, Alessio Oliviero, Giulia Tatafiore, Davide Torlo
The topics of the present proposal consist in modeling, numerical simulation and control of systems of utmost importance in industrial and physical applications and socio-economic life. The research is organized along three work packages (WP): WP1 Data-driven models by machine learning and filtering techniques; WP2 Development of advanced numerical schemes for hyperbolic systems; WP3 Numerical optimal control for real-world applications.
PNRR - MUR National Center for High Performance Computing, Big Data and Quantum Computing
“Numerical methods for hyperbolic problems with applications to control and traffic flow”
Investigators: Simone Cacace (local coordinator), Giuseppe Visconti
GNCS Project 2024
“Efficient numerical methods for Hamilton-Jacobi equations”
Investigators: Alessandro Alla (coordinator), Simone Cacace, Elisabetta Carlini