The framework developed within AROMA-CFD will provide attractive capabilities for several industrial and medical applications (e.g. aeronautical, mechanical, naval, off-shore, wind, sport, biomedical engineering and cardiovascular surgery as well), combining high performance computing (in dedicated supercomputing centers) and advanced reduced order modelling (in common devices), to guarantee real time computing and visualization. A new open source software library for AROMA-CFD will be created: ITHACA, In real Time Highly Advanced Computational Applications, enhancing current RBniCS educational and training capabilities. Our software can be found here.
The new ERC PoC ARGOS will further enhance the valorization of the ERC AROMA-CFD project.
Calcoli di un supercomputer su smartphone e tablet, ci penserà un algoritmo, May 2020
Calcoli di un supercomputer su smartphone, ci pensa un algoritmo, May 2020
Calculations of supercomputer on smartphone, algorithm take care of it, May 2020
TGR Leonardo Feb 7, 2020: about AROMA-CFD (minute 7:15s)
ERC at SISSA – Gianluigi Rozza, December 2019
Cross-fertilization of Ideas for real success stories, ESOF editorial by G.Rozza, May 2019
SIS-FVG and the initiative with MIT, May 2019
Plenary talk at SIAM CSE19 by PI Prof. Gianluigi Rozza, February 2019
FARE funding to three SISSA ERC grantees, December 21, 2017
A top european school, article in Platinum Aziende e Protagonisti (Il Sole 24 Ore) magazine, July, 2017. (Italian version)
EU-RESEARCH, June 2017
A new ERC to SISSA, SISSA press release, February 12, 2016
Two new ERC winners at SISSA (plus one), SISSA press release, February 19, 2016
National engineering council, province section, press release, February 24, 2o16
Minds Shaping the World, Alumni PoliMi, March 24, 2016
- S. Salavatidezfouli, S. Hajisharifi, M. Girfoglio, G. Stabile, and G. Rozza, “Applicable Methodologies for the Mass Transfer Phenomenon in Tumble Dryers: A Review“, 2023.
- N. Demo, M. Tezzele, and G. Rozza, “A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling“, 2023.
- A. Ivagnes, N. Demo, and G. Rozza, “Towards a Machine Learning Pipeline in Reduced Order Modelling for Inverse Problems: Neural Networks for Boundary Parametrization, Dimensionality Reduction and Solution Manifold Approximation“, Journal of Scientific Computing, 95(23), 2023.
- F. Pichi, F. Ballarin, G. Rozza, and J. S. Hesthaven, “An artificial neural network approach to bifurcating phenomena in computational fluid dynamics“, Computers & Fluids, pp. 105813, 2023.
- Prof Gianluigi Rozza (PI)
- Assistant Prof. Francesco Ballarin (2018-2021), Dr Giovanni Stabile (2020-2022)
- Post-doctoral research associates: Dr Martin Hess, Dr. Michele Girfoglio, Dr. Leonardo Scandurra, Dr. Mohammad Ghazizadeh
- Research associate: Mr. Marco Tezzele, Mr. Nicola Demo
- Past Internship Students (2019): Fabrizio Garotta, Julien Genovese, Giuseppe Infantino, Moreno Pintore,Giulio Ortali
- Past Master students (2016-17): Luca Venturi, Davide Torlo, Saddam Hijazi, Federico Pichi, Nirav Vasant Shah, Matteo Zancanaro, Maria Strazzullo, Giacomo Zuccarino, Giulia Meglioli, Giulio Ortali, Moreno Pintore, Julien Genovese, Giuseppe Infantino, Angelo Cetrangolo, Anna Ivagnes, Moaad Khamlich
- Former predoc students (2017): Matteo Zancanaro, Maria Strazzullo, Moaad Khamlich