194 citations · 201 across the 9 of their papers we have counts for
9 papers
Transferable inference of turbulence models for urban flows with the Parameter-Regularised Ensemble Kalman Filter
Emanuele Bombardi, Andrea Nóvoa, Luca Magri +1
The accurate simulation of urban flow is key to designing building ventilation, understanding cities' micrometeorology, and predicting pollutant dispersion. Reynolds-Averaged Navie…
Meshless data-driven decompositions with RBF-based inner products
Manuel Ratz, Alessandro Parente, Miguel Alfonso Mendez
Data-driven modal decompositions are useful tools for compressing data or identifying dominant structures. Popular ones like the dynamic mode decomposition (DMD) and the proper ort…
Generative artificial intelligence and hybrid models to accelerate LES in reactive flows: Application to hydrogen/methane combustion
Xiangrui Zou, Rodrigo Abadia-Heredia, Laura Saavedra +3
With increasing emphasis on carbon neutrality, accurate and efficient combustion prediction has become essential for the design and optimization of new generation combustion system…
Hierarchical Higher-Order Dynamic Mode Decomposition for Clustering and Feature Selection
Adrián Corrochano, Giuseppe D'Alessio, Alessandro Parente +1
In this work, a new algorithm based on the application of higher-order dynamic mode decomposition (HODMD) is proposed for feature selection and variables clustering in reacting flo…
A predictive physics-aware hybrid reduced order model for reacting flows
Adrián Corrochano, Rodolfo S. M. Freitas, Alessandro Parente +1
In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. This algorithm is based on a dimensionality reduction using Proper Orth…
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…