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We are a pioneering research and innovation centre in computational engineering, founded in 1987.
People at CIMNE: Meet the talent that makes it possible.
Research
Discover our latest research projects
Innovation
See how advanced simulation enhances nuclear safety
Community
Learn how the UNESCO Chair in Numerical Methods spearheads frontier innovation in the Global South
About
People at CIMNE: Meet the talent that makes it possible.
Community
With nine research clusters, three innovation units, and a global network with deep local roots, the CIMNE community is driving constant progress. Stay updated on scientific breakthroughs, international projects, and corporate developments shaping the future of engineering.
The International Centre for Numerical Methods in Engineering (CIMNE) and the Catalan Ministry of Home Affairs and Public Safety (Departament d'Interior i Seguretat Pública) have formally started exploring avenues for collaboration in the fields of emergency...
Experts at the CIMNE-UPM ETSII Lab (Aula CIMNE-UPM ETSII) have evaluated the effectiveness of Machine Learning (ML) and satellite imagery to assess water quality in inland settings. In a recent study, Ms. Laura Cáceres, Dr Jorge RodrÃguez Chueca and Dr David J....
The International Centre for Numerical Methods in Engineering (CIMNE) has launched DAMSHAI (Dam Structural Health Monitoring and Safety Assessment with an AI Agent), a three-year research project that will explore the application of artificial intelligence to critical...
Researchers at CIMNE's Machine Learning in Civil Engineering Research Group and the Flumen Institute have developed a new machine-learning based tool to classify off-stream reservoirs based on their potential risk of rupture. The work, carried out as part of the...
Researchers from the Machine Learning in Civil Engineering group at CIMNE have developed a new machine-learning based software to predict structural behaviour of dams, allowing for enhanced decision-making and minimizing safety risks of these critical infrastructures....
A group of researchers from the International Centre of Numerical Methods in Engineering (CIMNE) have proposed a new methodology for improving Machine Learning-based model forecasting of gound-level ozone (O3), a pollutant of photochemical origin that...
A publication co-authored by CIMNE researcher Fernando Salazar “CALA: Mejora de la seguridad hidrológica” (CALA: Improvement of the Hydrologic security) on the magazine Revista de Obras Públicas. The article explains the results of a...