SISSA mathLab

It was a great pleasure to have Alfio Quarteroni al SISSA today! ⭐️ We had the opportunity to meet him and talk about his researches in his long carrier. Thanks for have been here and meet us!

We took part at the GNCS-SIAM Chapters Meeting 2025! Federico Pichi was one of the speakers with his presentation entitled “Graph-based machine learning approaches for model order reduction”. Also Dario Coscia gave a contributed talk entitled “Bayesian Autoregressive and Recurrent Neural Networks for Neural PDE Solvers Uncertainty Quantification and Drug Discovery”, and Anna Ivagnes with her talk “Parametric Intrusive Reduced Order Models Enhanced with Machine Learning Correction Terms”. We also have Gabriele Codega, Filippo Olivo, Dario Coscia, Pierfrancesco Siena, Guglielmo Padula, Giovanni Canali, Nico Clinco, Isabella Carla Gonnella and Lorenzo Tomada with their posters!

Il progetto di ricerca Hi4TrAIning rivoluziona la formazione del personale a bordo nave tramite l’utilizzo di diverse tecnologie avanzate. 💡 Sviluppato dalle aziende triestine ENGYS e MES, grazie al supporto di SMACT, HCI Lab - Human-Computer Interaction Laboratory - del Dipartimento di Scienze Matematiche, Informatiche e Fisiche dell’Università degli Studi di Udine, SISSA mathLab e Maritime Technology Cluster FVG - mareFVG, il progetto prevede l’uso di strumenti innovativi come modellazione 3D, realtà virtuale immersiva, analisi ad alta fedeltà di flussi di gas mediante tecniche e strumenti di fluidodinamica computazionale e modelli a ordine ridotto. L'obiettivo del progetto è migliorare la gestione delle emergenze su navi moderne, contribuendo a garantire maggiore sicurezza e sostenibilità attraverso l’uso di un software all’avanguardia. ✅ Nel corso dei mesi, per realizzare la piattaforma software Hi4TrAIning sono stati creati ambienti 3D dettagliati che riproducono spazi interni e componenti delle navi dotate di propulsione a gas. 🚢 Questi modelli sono stati integrati in un ambiente di realtà virtuale avanzato, progettato per offrire al personale di bordo esperienze virtuali che simulano situazioni operative complesse e di emergenza, riducendo i tempi di apprendimento e aumentando al contempo l'efficacia della formazione. La piattaforma è stata progettata per essere modulare e personalizzabile, consentendo di monitorare e quantificare la preparazione dello staff tramite KPI dedicati e, nel prossimo futuro, potrebbe espandere le sue applicazioni in diversi altri settori industriali. 🌐 #progettoIRISS #innovazione #realtàvirtuale #VR #sicurezza #tecnologia

Even in 2024 our Gianluigi Rozza is listed in the 2% of Top scientists ranking! He reached the nr. 148 out of 16443 authors in Numerical & Computational Mathematics. Congratulations!! 👏 https://lnkd.in/dSqdPTe4

Many congratulations to Giulio Ortali and Sajad Salavatidezfouli who defended their thesis at SISSA!! 🎉🥂 Giulio’s thesis entitled “Data-driven modeling of emergent statistical behavior in fluid dynamics: from turbulence to dense emulsions”, in collaboration with Eindhoven University of Technology and supervised by Nicola Demo, Gianluigi Rozza, Federico Toschi and Alessandro Corbetta. Sajad’s thesis entitled “Integration of Computational Techniques for System Modeling and Data Assimilation in Industrial and Life Science Problems” was in collaboration with Electrolux Group and supervised by Giovanni Stabile, Michele Girfoglio , Arash Hajisharifi , Terenzio Girotto , Gianluca Ricci and Gianluigi Rozza.

A new work has been published on Computers & Mathematics with Applications! The paper entitled "A hybrid reduced-order model for segregated fluid-structure interaction solvers in an ALE approach at high Reynolds number" has written by Valentin Nkana, Giovanni Stabile, Andrea Mola and Gianluigi Rozza. This study introduces a first step for constructing a hybrid reduced-order models (ROMs) for segregated fluid-structure interaction in an Arbitrary Lagrangian-Eulerian (ALE) approach at a high Reynolds number using the Finite Volume Method (FVM). The ROM is driven by proper orthogonal decomposition (POD) with hybrid techniques that combines the classical Galerkin projection and two data-driven methods (radial basis networks, and neural networks/ long short term memory). Results demonstrate the ROM's ability to accurately capture the physics of fluid-structure interaction phenomena. This approach is validated through a case study focusing on flow-induced vibration (FIV) of a pitch-plunge airfoil at a high Reynolds number.

We’re delighted to announce that our latest research work, "A Hybrid Reduced-Order Model for Segregated Fluid-Structure Interaction Solvers in an ALE Approach at High Reynolds Number," is now officially published and available online! This project has been a rewarding collaboration with Giovanni Stabile, Andrea Mola, and Gianluigi Rozza. Together, we’ve addressed the complexities of fluid-structure interaction with moving meshes at very high Reynolds numbers, introducing a novel hybrid reduced-order modeling approach to enhance computational efficiency, robustness, and accuracy. If you're passionate about advanced numerical methods, reduced-order modeling, or fluid-structure interaction in moving domains, we’d love for you to check it out! 💡 Feel free to share your thoughts or reach out—we’re eager to hear your feedback. 👉 [https://lnkd.in/dRVs8Asz] #Research #ReducedOrderModeling #FluidStructureInteraction #NumericalMethods #HighReynoldsNumber #MovingMeshes #MachineLearning #RadialBasisFunctions

On behalf of the other two Chairs of the 16th #DynamicalSystems applied on #Biology and #NaturalSciences #DSABNS2025 (https://lnkd.in/eEKRai_f) Lucia Russo and Francesco Giannino, I would like to thank each one of the partecipants for making this conference truly special! With 240 partecipants from all over the world, we’ve had the pleasure over the past 5 days to attend great talks, sharing ideas, building collaborations and friendships that will last far beyond this event. The amazing public lectures given by Alfio Quarteroni & Julyan Cartwright, the plenary talks by Maíra Aguiar, Roberto Barrio, Konstantin Blyuss, Bruno Buonomo Mario di Bernardo, Yuliya Kyrychko, Roberto Natalini, Viola Priesemann, Antonello Provenzale, Gianluigi Rozza, Nico Stollenwerk, Uffe Høgsbro Thygesen, invited speakers, and all partecipants shared groundbreaking insights giving food for thought to open challenging problems ranging from #machinelearning and #complexsystems to #ecology #epidemiology, #biology and more. A special thank you goes to the local organizing committee, young researchers Annalisa Iuorio , Alessandro Della Pia , Dimitris Patsatzis, Ph.D. , Gianluca Fabiani , and Kyriakos Georgiou, Ph.D. who have worked tirelessly on all aspects of the conference to make things happen! We are also grateful to the international organizing committee Maíra Aguiar, Carlos Braumann, Paula Patricio, Bob Kooi, Andrea Pugliese, Nico Stollenwerk, and Ezio Venturino for the help and support! Thank you all once again!

Gianluigi Rozza is today at Università degli Studi di Napoli Federico II (UniNa) / University of Naples Federico II for the 16th Dynamical Systems applied on Biology and Natural Sciences. DSABNS is a series of international conferences founded in 2010 targeted at the development and implementation of mathematical methodologies to model and analyse the dynamics of complex problems in bio and natural sciences https://lnkd.in/dC49HDga

Join us at ICOSAHOM 2025 (https://lnkd.in/dKubQAJd) in Montreal (July 13–18, 2025) for our Mini Symposium, "Accelerating High-order CFD Simulations with Surrogate Modelling"! High-order methods like DG/FR/SD/WENO/TENO revolutionize CFD with high accuracy, but their computational demands can be a challenge. Our MS explores solutions like Reduced Order Models (ROMs), hybrid techniques, and machine-learning-enhanced solvers to address these challenges. Co-organized with Pasquale Claudio Africa, Federico Pichi, Michele Girfoglio, and Gianluigi Rozza (SISSA mathLab), this session invites contributions on: *Intrusive & non-intrusive ROMs *Hybrid & data-driven techniques *Multi-fidelity modeling & h/p refinement Submit your abstract to MS 129 before 01/31: https://lnkd.in/du6Q-R23. We look forward to your insights and collaborations!