Highlights
Projects
Over the years, the Rozza Group has contributed to a large number of research projects spanning advanced mathematical modeling, scientific computing, and machine learning. This commitment has led to the development of a broad ecosystem of computational frameworks and methodological platforms, reflecting the group’s ability to combine theoretical research with robust computational tools and enable applications across multiple domains.
ROSA
Reduced Order and Surrogate Methods for Advanced Applications
MUR FIS-3
2026–2030
New project
ROSA is a recent research initiative aimed at advancing surrogate modeling and reduced order methodologies for the simulation of complex physical systems. ROSA will combine data-driven techniques, artificial intelligence, and physics-based modeling to build computationally efficient surrogate models while preserving physical consistency and robustness. Particular attention will be devoted to challenges such as limited or noisy data, uncertainty quantification, and the preservation of fundamental physical properties.
ATLAS
Advanced Toolkit for Large-scale Accelerated Simulation in cardiovascular modeling
ERC Seal of Excellence
2023–2024
ATLAS is a real-time platform for cardiovascular modeling built on the reduced order modeling and data-driven methods developed within AROMA-CFD. Its purpose is to make advanced cardiovascular simulations more accessible in clinical and research settings through a cloud-based environment with a simple web interface. Available use cases on the platform include the thoracic aorta, carotid artery, aorto-femoral district and coronary circulation. ATLAS supports clinicians and researchers in applications such as diagnosis, treatment planning and personalized medicine, while significantly reducing computational costs and response times. The platform also opens the way to future integrations with technologies such as augmented reality and digital twins for healthcare.
ARGOS
Advanced Reduced Groupware Online Simulation
ERC Proof of Concept
2022–2024
Developed with the support of Fast Computing, ARGOS is a real-time platform for numerical modeling and data visualization built on top of the methods and software developed within the scope of AROMA-CFD. ARGOS introduced the computational webserver as an intermediate layer in the offline-online paradigm, making reduced order models more accessible through the web. The platform combines advanced machine learning algorithms, cloud computing, and a user-friendly interface, enabling real-time interaction with complex simulations without requiring direct access to the underlying computational infrastructure. ARGOS also includes an educational environment featuring interactive applications focused on dynamic mode decomposition, clustering, Navier-Stokes interpolation, and convective streams.
FARE-X-AROMA-CFD
MUR FARE
2018–2020
FARE-X-AROMA-CFD expanded the research directions introduced by AROMA-CFD, focusing on advanced numerical methods for parametric partial differential equations in increasingly complex fluid dynamics problems. In particular, the project addressed two major challenges: the reduction of high-dimensional parameter spaces and the development of reduced order methods for compressible flows. These advancements made it possible to tackle more complex aerodynamic and multiphysics applications, including aeroacoustics, turbomachinery and aero-thermo-elasticity, while also improving optimization, flow control and uncertainty quantification workflows. The project further extended the capabilities of ITHACA, strengthening its role as an open-source environment for advanced reduced order modeling.
AROMA-CFD
Advanced Reduced Order Methods with Applications in Computational Fluid Dynamics
ERC Consolidator Grant
2016–2022
AROMA-CFD was dedicated to overcome the limitations of model order reduction techniques and develop applications in industry, medicine, and applied sciences. Involving 15 universities and one Italian hospital, one of its main outcomes was ITHACA (In real Time Highly Advanced Computational Applications), an open-source library designed to support reduced order modeling workflows in computational fluid dynamics, which expanded the educational and training capabilities of RBniCS. AROMA-CFD also led to the consolidation of the online-offline paradigm, which separates the computationally expensive generation of reduced models (online phase, performed with a HPC cluster) from their rapid evaluation in real time (offline phase, performed on a user device).
Publications
The following publications represent some of the most significant scientific contributions of the Rozza Group. They highlight key methodological advances, influential applications, and research directions that have shaped the group’s work throughout the years.
2025Mesh-informed Reduced Order Models for aneurysm rupture risk prediction Journal of Computational and Applied Mathematics |
2024Computational study of numerical flux schemes for mesoscale atmospheric flows in a Finite Volume framework Communications in Applied and Industrial Mathematics |
2023An extended physics informed neural network for preliminary analysis of parametric optimal control problems Computers & Mathematics with Applications |
2021Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing Journal of Marine Science and Engineering |
2020Efficient geometrical parametrization for finite-volume-based reduced order methods International Journal for Numerical Methods in Engineering |
2018Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations Computers & Fluids |
CSE Software
The Rozza Group has developed a number of open-source software libraries and computational tools designed to translate methodological research into accessible and reusable resources.
ITHACA-SEMReduced order modeling and data-driven methods for high-fidelity simulations based on spectral element methods |
ITHACA-FVReduced order modeling for computational fluid dynamics based on finite volume discretizations |
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 PyDMDDynamic Mode Decomposition techniques for the analysis and forecasting of complex dynamical systems |
 EZyRBAccessible reduced order modeling workflows for interpolation, approximation, and parametric studies |
 PINAPhysics-informed neural networks for differential equations and scientific machine learning |
