Open-source modelling, simulation, reduced-order modelling, and data-driven tools for engineering and biomedical applications.
🌐 Website: https://modelflows.github.io/modelflowsapp/
The ModelFLOWs website provides open-source software, tutorials, workflows, notebooks, datasets, publications, and educational resources related to:
- Fluid Mechanics
- Reduced-Order Modelling (ROM)
- Modal Decomposition
- Data-Driven Modelling
- Machine Learning for Engineering
- Computational Fluid Dynamics (CFD)
- Biomedical Applications
- Combustion
- Urban Flows and Air Pollution
- Aerospace Engineering
https://modelflows.github.io/modelflowsapp/software/
Includes:
- Applications
- Notebooks
- Databases
- ModelFLOWs-app (legacy)
https://modelflows.github.io/modelflowsapp/software/applications/
Application-oriented workflows and tutorials, including:
- Urban Flows
- Combustion
- Cardiac Pathology
- Air Pollution
- Fluid Dynamics and Aerospace Engineering
Each application provides access to:
- CFD & High-Fidelity Simulations
- AI & Data-Driven Models
- Tutorials
- Videos
- Resources & Databases
- Publications
https://modelflows.github.io/modelflowsapp/research/
Research highlights, publications, project updates, and scientific contributions.
We welcome contributions from members of the ModelFLOWs team.
Tutorials, videos, datasets, and resources can be added through the Applications Hub.
Please follow the contribution guidelines provided during the workshop.
Soledad Le Clainche
Project Lead and Scientific Coordinator
Website: soledadleclainche.com
The ModelFLOWs platform is developed and maintained through contributions from researchers, PhD students, collaborators, and alumni.
For the current team and contributor information, please visit:
https://modelflows.github.io/modelflowsapp/about/
Individual tutorials, workflows, notebooks, and software repositories include detailed authorship and contributor information.
ModelFLOWs is an open-science initiative focused on developing advanced modelling, simulation, reduced-order modelling, and data-driven methodologies for engineering and biomedical applications.