Real Time Reduced Order Computational Mechanics eBook
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About the book
Imprint
Collection
n.c
Publication date
2024-05-23
Pages
180 pages
Print ISBN
9783031498916
Language
English
Ebook informations
EAN PDF
9783031498923
Price
£109.50
EAN EPUB
9783031498923
Price
£109.50
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Gianluigi Rozza received his Ph.D. in Applied Mathematics at EPF Lausanne, Switzerland, in 2006 and he is currently full professor in Numerical Analysis and Scientific Computing at SISSA, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy. His research focuses on reduced order methods in computational mechanics, including uncertainty quantification, automatic learning, optimal control, inverse problems and emerging technologies like digital twin in industry.

Francesco Ballarin received his Ph.D. in Mathematical Models and Methods in Engineering at Politecnico di Milano, Italy in 2015, and is currently assistant professor in Numerical Analysis in the Department of Mathematics and Physics at Università Cattolica del Sacro Cuore, Brescia, Italy. His research focuses on reduced order models for parametrized problems in computational fluid dynamics. He is a passionate developer of open source software, which becomes an integral part of his research.

Leonardo Scandurra received his Ph.D. at Università degli Studi di Catania working on numerical methods for flows with different Mach number in gas dynamics. He contributed to different teaching activities at the HHU in Düsseldorf, where in particular he contributed to introduce a CFD course. He is currently a senior researcher at Engys srl in Trieste as software developer for CFD problems. His main research interests focus on numerical methods applied to statistical convergence assessment and Quantum CFD.

Federico Pichi received his Ph.D. in Mathematical Analysis, Modelling and Applications at SISSA, and he is currently a postdoctoral researcher at EPFL - École Polytechnique Fédérale de Lausanne in the MCSS group of Prof. Jan S. Hesthaven. His research interests include projection-based and data-driven reduced order models in computational science and engineering, with applications to parametrized bifurcating problems. He also develops scientific machine learning approaches bridging numerical analysis and novel architectures.

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