ModelFLOWs is a research group at Universidad Politécnica de Madrid (Spain), School of Aerospace Engineering, specializing in data analysis, reduced order modeling, machine learning, artificial intelligence and computational fluid dynamics (CFD). Our mission is to develop innovative solutions with real-world impact, particularly in aerospace engineering, combustion, urban air pollution mitigation, and personalized medicine.
We are committed to open science and education. Whether you're a Master’s or PhD student looking to deepen your knowledge or a senior researcher exploring new methodologies, our resources are designed to support you.
Explore Our Resources
- Notebooks & Codes: Reference implementations, interactive notebooks, and tutorials covering ModelFLOWs methodologies and applications.
- Applications: Ready-to-use software for modal decomposition, reduced-order modeling, forecasting, and data-driven analysis.
- Datasets: Curated datasets and benchmark cases from fluid dynamics, medicine, urban environments, and other complex dynamical systems.
- Tutorials & YouTube Channel: Step-by-step guides, educational material, and video demonstrations of ModelFLOWs methodologies and software.
- ModelFLOWs-app is an open-source ecosystem integrating software, applications, datasets, and educational resources for the analysis and modeling of complex dynamical systems.
Join us in pushing the boundaries of computational modeling and data-driven science!
Citation
If you use ModelFLOWs-app in your research, please cite:
A. Hetherington, A. Corrochano, R. Abadía-Heredia, E. Lazpita, E. Muñoz, E. Díaz, E. Maiora, M. López-Martín, S. Le Clainche. ModelFLOWs-app: data-driven post-processing and reduced order modelling tools. Computer Physics Communications, 301, 109217, 2024.