SISSA MathLab

🆕 A new article by Lander Besabe, Michele Girfoglio, Simona Perotto, Annalisa Quaini, and Gianluigi Rozza has been published in Advances in Computational Science and Engineering (): An isotropic recovery-based error estimator algorithm for mesh adaptation in a finite volume environment with application to atmospheric flows.

💨 The paper introduces an adaptive mesh refinement method for atmospheric simulations, dynamically increasing resolution where needed. The approach improves accuracy and stability while reducing computational costs compared with uniformly fine meshes.

🔗 Read the full article:
https://www.aimsciences.org/article/doi/10.3934/acse.2026012

📢 Looking for an internship in AI and engineering?

💼 NVIDIA is offering a 6-month internship in Zurich, Switzerland for Master's students in Mathematics, Data Science and AI, Computer Science, and Engineering.

⚙️ The internship will be co-supervised by SISSA mathLab and will focus on AI surrogate models and agentic approaches for the preprocessing of computer-aided engineering (CAE) applications.

📅 The starting date is flexible and can be agreed upon until December 1, 2026.

📩 For more information and applications, please contact Davide Fransos and Gianluigi Rozza:
https://www.linkedin.com/in/davidefransos/
https://www.linkedin.com/in/gianluigi-rozza-8447903/

🌍 This week, a large part of our group will be in Munich for WCCM-ECCOMAS 2026, the 17th World Congress on Computational Mechanics and 10th European Congress on Computational Methods in Applied Sciences and Engineering, supported by IACM (International Association for Computational Mechanics), European Community on Computational Methods in Applied Sciences, and GACM (German Association for Computational Mechanics).

🎤 Throughout the congress, our Gianluigi Rozza, Pasquale C. Africa, Dario Coscia, Lorenzo Fabris, Isabella C. Gonnella, Rahul Halder, Anna Ivagnes, Hammad Khaliq, Gaspare Li Causi, Federico Pichi, and Lorenzo Tomada will present contributions spanning reduced order modeling, scientific machine learning and uncertainty quantification—with applications including naval engineering, sustainable mobility, and cardiac electrophysiology.

📍 Gianluigi Rozza and Federico Pichi are also chairing minisymposium series during the congress. In addition, Gianluigi will chair the plenary lecture "Agentic Scientific Machine Learning" by George Karniadakis.

👉 Discover the full scientific program: https://wccm-eccomas2026.org/event/programme

UPDATE: Applications are closed.

📢 A new opportunity for early-career mathematicians at SISSA - Scuola Internazionale Superiore di Studi Avanzati!

🆕 The SISSA Mathematics Area is offering a new PhD fellowship in mathematical analysis, modeling and applications, funded by Fincantieri SpA.

🚢 The research project deals with the development of surrogate methods for the parametric optimization of the structural analysis of passenger ships.

⏰ The application deadline is August 27, 2026.

🔗Apply here: https://www.sissa.it/bandi/selection-conferment-phd-fellowship-funded-fincantieri-spa

www.sissa.it

🆕 A new review article by Shenhui Ruan, Andreas G. Class, and Gianluigi Rozza has been published in “Archives of Computational Methods in Engineering” (Springer Nature): “A Structured Review of Reduced Order Modeling for Domain Decomposition Problems: State of the Art and Perspectives”.

📚 The review provides a comprehensive overview of reduced order modeling techniques combined with domain decomposition, an approach that accelerates large-scale engineering simulations by dividing complex problems into smaller subdomains and constructing local reduced models.

👉 Read the full article:
https://link.springer.com/article/10.1007/s11831-026-10690-9



𝘐𝘮𝘢𝘨𝘦 𝘢𝘥𝘢𝘱𝘵𝘦𝘥 𝘶𝘯𝘥𝘦𝘳 𝘵𝘩𝘦 𝘊𝘊 𝘉𝘠 4.0 𝘭𝘪𝘤𝘦𝘯𝘴𝘦: http://creativecommons.org/licenses/by/4.0/

🆕 A new article by Lorenzo Tomada, Federico Pichi, and Gianluigi Rozza has been published in the Journal of Computational Physics (Elsevier): “Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs”.

🧠 The paper introduces a data-driven reduced-order modeling framework that combines graph neural networks with a low-dimensional latent representation of dynamical systems. The proposed approach enables the efficient reconstruction of solutions to parameterized time-dependent PDEs, including problems defined on complex geometries, while reducing computational costs.

👉 Read the full article:
https://www.sciencedirect.com/science/article/pii/S0021999126005024

🆕 A new article by Harsh*th Gowrachari, Mattia Giuseppe Barra, Giovanni Stabile, Gianluca Bazzaro and Gianluigi Rozza is now available online as a pre-proof in Results in Engineering (Elsevier): “Data-driven reduced order model for residence time distribution analysis of an industrial-scale continuous casting tundish”.

⚙️ The paper presents a data-driven reduced order model for predicting the residence time distribution in an industrial continuous-casting tundish, a key component of the steel production process. By accurately reproducing the results of full-order simulations at a fraction of the computational cost, the proposed approach enables efficient real-time analysis and supports process monitoring and optimization in industrial environments.

👉 Read the article:
https://www.sciencedirect.com/science/article/pii/S2590123026025867

📍 In the past few days, SISSA hosted the workshop “Mathematics and Mechanics of Active and Dissipative Matter”, organized by our Davide Riccobelli along with Giovanni Noselli’s group, with the support of INdAM (Istituto Nazionale di Alta Matematica).

🧩 The event brought together researchers working across mathematics, mechanics, and biology to discuss the modeling of biological tissues and active materials. Topics included soft matter, morphogenesis, mechanobiology, fracture, viscoelasticity and multiphysics systems.

🤝 The workshop provided an opportunity to exchange ideas and perspectives across different disciplines, highlighting the role of mathematical modeling in understanding complex physical and biological phenomena.

🆕 A new article by Isabella Carla Gonnella, Moaad Khamlich, Federico Pichi, and Gianluigi Rozza has been published in the Journal of Scientific Computing (Springer Nature): “A Stochastic Perturbation Approach to Nonlinear Bifurcating Problems”.

🧠 The paper proposes a stochastic perturbation approach based on Polynomial Chaos Expansion to represent multiple solution branches in bifurcating systems through a single perturbed model, reducing the need for repeated, computationally expensive simulations.

👉 Read the full article:
https://link.springer.com/article/10.1007/s10915-026-03338-0

📢 New opportunities for early-career researchers in mathematics at SISSA - Scuola Internazionale Superiore di Studi Avanzati!

The SISSA Mathematics Area is currently recruiting young researchers interested in mathematical analysis, modeling and applications.

🔹 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐂𝐨𝐧𝐭𝐫𝐚𝐜𝐭
A research position is available under the supervision of Prof. Gianluigi Rozza, focusing on scientific computing, surrogate modeling and scientific machine learning.
⏰ Deadline: June 15, 2026
🔗 https://www.sissa.it/bandi/selezione-pubblica-titoli-conferimento-di-n-1-contratto-di-ricerca-area-matematica-ref-prof-2

🔹 𝐏𝐡𝐃 𝐢𝐧 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬, 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
Applications are also open for a new admission session to the SISSA PhD program, with two fellowships still available.
⏰ Deadline: August 27, 2026
🔗 https://www.sissa.it/bandi/ammissione-ai-corsi-di-phd-della-sissa-lanno-accademico-202627-admission-sissa-phd-courses