Matteo Giacomini
Matteo Giacomini

Associate Professor of Computational Engineering at Universitat Politècnica de Catalunya
Affiliated researcher at CIMNE - Centre of Excellence Severo Ochoa
Affiliated faculty at IMTech - Institute of Mathematics of UPC-BarcelonaTech

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matteo.giacomini@upc.edu

Universitat Politècnica de Catalunya
Campus Nord, Edifici C2-203A
Jordi Girona 1
Barcelona (Spain), E08034
Tel: +34 93 40 16244

Scopus ID: 57038403600
Google Scholar ID: WXgzPp0AAAAJ
ORCID: 0000-0001-6094-5944
Researcher ID: N-7839-2016

Publication list (with PDF)
Preprints @ arXiv

https://sites.google.com/site/matteogiacominiweb/home

Credible high-fidelity data-driven models

Education

2016 - PhD in Applied Mathematics at Ecole polytechnique, Université Paris-Saclay (France)

2013 - MSc in Mathematical Engineering at Politecnico di Milano (Italy)

2010 - BSc in Mathematical Engineering at Politecnico di Milano (Italy)

Research interests

High-order methods: finite element, discontinuous Galerkin, hybridisable discontinuous Galerkin. 

Low-order methods: finite volume, face-centred finite volume. 

Error & adaptivity: a posteriori estimates, goal-oriented estimates, mesh adaptation, anisotropic meshes, degree adaptation. 

Dimensionality & complexity reduction: scientific machine learning, reduced order models, proper generalised decomposition, multi-fidelity, domain decomposition. 

PDE-constrained optimisation: shape optimisation, topology optimisation, multiobjective optimisation, moving interfaces. 

Development of open-source CSE software. 

Applications: computational fluid dynamics, computational solid mechanics, multiphysics, image segmentation, industrial problems, sustainability.

Additional information

Affiliated with CIMNE research groups Innovative Algorithms for Fast Accurate Computing and Credible data-driven models.

Affiliated faculty within IMTech research line on Mathematical Modelling and Numerical Analysis.

Co-chairperson of Math 2 Product (M2P): ECCOMAS-IACM Conference on Emerging Technologies in Computational Science for Industry, Sustainability and Innovation.