ModelFLOWs is a research group whose main promoter and tutor is Soledad Le Clainche and was formed in the Dept. of Applied Mathematics at the School of Aerospace Engineering from Universidad Politécnica de Madrid (UPM). This team uses different data-driven methods, i.e. modal decompositions, machine learning architectures and data assimilation tools, to generate hybrid reduced order model grounded in physics. These models are suitable to study the physics of the dynamical systems, to reconstruct and repair databases and for temporal forecasting. The fact that these models are grounded in physics makes them robust and with strong generalization capabilities. They have been successfully tested for various applications including complex flows (turbulent, reactive, etc.) and any type of non-linear dynamical system. ModelFLOWs also have strong experience in comptational fluid dynamics (CFD), flow control and in the development of new tools and methods for data analysis.
List of contributors
The following members have contributed in the development of ModelFLOWs-app, an open sourced software for data post-processing, patterns identification and development of reduced order models:
- Adrián Corrochano
- Eneko Lazpita
- Ashton Ian Hetherington
- Rodrigo Abadia-Heredia
- Eva Muñoz Salamanca
- Paula Díaz Morales
- Egoitz Maiora
- Soledad Le Clainche
For more information, please visit the official ModelFLOWs website