We compute fluid dynamics simulations on open sources (i.e. Nek5000, OpenFOAM). 

We apply reduced order model (i.e. POD, DMD, HODMD) to analyze databases of various fields to extract pattens, to model dynamical systems and to understand the physics of the problem under study.

We employ machine learning and neural networks to reconstruct and predict a wide spectrum of databases. 

• PhD student in Aerospace Engineering
• Academic background: BSc in Mathematics & MSc in Industrial Mathematics
• Research fields: Data Analysis, Machine Learning, forecasting models applied to fluid dynamics problems, building robust algorithms for multiple types of flows
• International collaborations with: KTH and ULB

• PhD student in Aerospace Engineering
• Academic background: BSc in Aerospace Engineering & MSc in Industrial Mathematics
• Research fields: CFD, Machine Learning, Reduced Order Modeling, Urban Flows, Channel Flow with Heat Transfer, Heart Flow
• National and International collaborations: KTH, Purdue University, CNIC, Hospital Puerta de Hierro and Harvard University

• PhD Student in Aerospace Engineering

• Academic background: B.Tech in Mechanical Engineering & MSc in Electromechanical Engineering

• Research fields: Optimization, Reduced Order Models, Computational Fluid Dynamics, Machine Learning, Data Analysis

• Marie Skłodowska-Curie Actions fellow

• International collaborations with: Air Quality Consultants and ARUP

• PhD student in Aerospace Engineering
• Academic background: BSc in Mechanical Engineering & MSc in Industrial Engineering
• Research fields: Machine Learning, Data Analysis, MLOps, Big Data, complex flows, other complex dynamical systems
• Industrial collaboration with: Boeing Research & Technology Europe

• PhD student enrolled at Okinawa Institute of Science and Technology (OIST), Japan
• Academic background: BSc in Aerospace Engineering & MSc in Industrial Mathematics
• Research fields: CFD in complex fluids. Non-newtonian jet flows. Data Analysis, Reduced Order Models to speed up CFD simulations.
• International collaboration with: UPM (PhD thesis co-supervision)

• PhD student in Mathematics Engineering, Statistics and Operational Investigation (IMEIO)
• Academic background: BSc in Aerospace Engineering & MSc in Thermophysics.
• Research fields: Deep Learning, Reduced Order Modelling, CFD, Flow Control.
• International collaboration with: SUSTech

• PhD Student in Aerospace Engineering

• Academic background: BSc in Mechanical Engineering & MSc in Industrial Mathematics

• Research fields: CFD, pollutant dispersion, data analysis, machine learning, fluid dynamics forecasting models, uncertainty estimation and building robust algorithms for different types of flows

• Marie Skłodowska-Curie Actions fellow

• International collaborations with: KTH and ULB

• PhD Student in Aerospace Engineering

• Academic background: BSc in Mechanical Engineering, MSc in Project Management, MSc in Mechanical Engineering, Design and Simulation

• Research fields: Optimization, Reduced Order Models, Computational Fluid Dynamics, Machine Learning, Data Analysis

• Marie Skłodowska-Curie Actions fellow

• International collaborations with: ULB and BuildWind

• PhD student in MODELAIR Project.

• Academic background: BSc in Mechanical Engineering and Energy and MSc in Mechanical Engineering specialized in simulation

• Research fields: CFD, Data Analysis, Machine Learning, forecasting models applied to fluid dynamics problems

• Marie Skłodowska-Curie Actions fellow

• International collaborations with: ULB, KTH, UoB.

• PhD student in Aerospace Engineering

• Academic background: BTech in Mechanical Engineering & MTech in Mechanical Design

• Research field: Data Analysis, Dimensionality reduction, Reduced order modelling, Combustion, Machine Learning, Deep Learning.

• Marie Skłodowska-Curie Actions fellow

• International collaborations with: CNRS and MITIS

• PhD student in Aerospace Engineering
• Academic background: BSc in Mechanical Engineering & MSc in Fluid Mechanics
• Research fields: Machine Learning, numerical simulations using FEM and CFD methods. 
• Experience: work in international research facilities, governmental institutions as well as in private and state-owned companies 

• PhD student in Mathematics Engineering, Statistics and Operational Investigation (IMEIO)

• Academic background: BSc in Physics, MSc in Physics and Mathematics, MSc in Mathematics and Applications.

• Research Fields: Deep Learning, Applied Mathematics, Computational Physics, Data Analysis.

• Industrial experience: GNSS Junior Engineer associated to the SouthPAN Project at GMV

• PhD student in Aerospace Engineering
• Academic background: BSc in Aerospace Engineering & MSc in Industrial Mathematics 
• Research fields: Reinforcement Learning, Multi-Agent Learning, AI for Science, Dynamical Systems & Control. 
• National and International collaborations: Spanish National Institute for Aerospace Technology (INTA), KTH. 
• Industrial collaboration with: Airbus 

• PhD in Communication Technologies and Services from ETSIT-UPM

• Research fields: Big Data, Computer Vision, Deep Learning, Machine Learning, and Video and Image Analysis and Processing

• Research experience: Research Associate at the Image Processing Group, GTI-UPM

• National and International collaborations: Participation in the "Innolog” project, from the EIT Raw Materials. “Dirección General de Tráfico” (DGT) and Airbus

• PhD in Aerospace Engineering from Beijing Institute of Technology (BIT).

• Research fields: Hydrogen Combustion, CFD Simulation, Machine Learning.

• Research experience: Assistant Professor in School of Aerospace Engineering at Xi'an Jiaotong University (XJTU), China.

• National and International collaborations: BIT, NPU, SCU.

• Visiting researcher in School of Aerospace Engineering at Universidad Politécnica de Madrid (UPM).

• PhD student in Aerospace Engineering
• Academic background: BSc in Aerospace Engineering & MSc in Industrial Mathematics
• Research fields: Data Analysis, Machine Learning, Reduced Order Modeling, data-driven tools for patterns detection, turbulent combustion, complex flows
• International collaborations with: KTH, IMFT, ULB, OIST, Cranfield University and University of Sydney

• PhD student in Engineering and Technology Science at Université Libre de Bruxelles (ULB), Belgium.
• Research fields: Machine Learning, Data Analysis, Reduced Order Modeling on reactive and non-reactive flows for pattern extraction and speeding up combustion

• PhD student in Applied Health and Life Sciences at the University of Verona (UV), Italy.
• Research fields: environmental epidemiology, biostatistics, air pollution,  aerobiology, climate change

• PhD in Chemical Engineering from IIT Guwahati
• Research fields: CFD, Biofluid Mechanics, Cerebrovascular and heart hemodynamics
• Research experience: Research Associate at the Department of Neurosurgery, SCTIMST, India
• Marie Skłodowska-Curie Actions fellow
• International collaborations: IIT Madras and SCTIMST
• Alumni: Post-doc in ModelFLOWs

• PhD in Applied Mathematics
• Research fields: Machine Learning, Reduced Order Models, fluid dynamics applied to medical imaging
• National collaboration with: CNIC (National Center of Cardiological Research)
• Alumni: PhD in ModelFLOWs

• BSc student in Aerospace Engineering at UPM
• BSc thesis completed in ModelFLOWs in 2024: Tools for data repairing and resolution enhancement
• Alumni: BSc thesis completed in ModelFLOWs in 2024: machine learning in fluid dynamics

• MSc student in Aeronautical Engineering at UPM. 
• Research fields: CFD simulations, turbulent flows and urban environments. 

• International collaboration with: TU-Delft
• Alumni: MSc thesis completed in ModelFLOWs in 2024: CFD in urban flows