194 citations · 201 across the 9 of their papers we have counts for
4 papers · 1 filter
Improving aircraft performance using machine learning: a review
Soledad Le Clainche, Esteban Ferrer, Sam Gibson +3
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (expe…
Advancing Reacting Flow Simulations with Data-Driven Models
Kamila Zdybał, Giuseppe D'Alessio, Gianmarco Aversano +4
The use of machine learning algorithms to predict behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems,…
Local manifold learning and its link to domain-based physics knowledge
Kamila Zdybał, Giuseppe D'Alessio, Antonio Attili +3
In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain t…
Higher order dynamic mode decomposition to model reacting flows
Adrián Corrochano, Giuseppe D'Alessio, Alessandro Parente +1
In this work, the application of the multi-dimensional higher order dynamic mode decomposition (HODMD) is proposed for the first time to analyse combustion databases. In particular…