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This article presents an innovative open-source software namedModelFLOWs-app, written in Python, which has been created and tested togenerate precise and robust hybrid reduced order models (ROMs) fullydata-driven. By integrating modal decomposition and deep learning methods indiverse ways, the software uncovers the fundamental patterns in dynamicsystems. This acquired knowledge is then employed to enrich the comprehensionof the underlying physics, reconstruct databases from limited measurements, andforecast the progression of system dynamics. These hybrid models combineexperimental and numerical database, and serve as accurate alternatives tonumerical simulations, effectively diminishing computational expenses, and alsoas tools for optimization and control. The ModelFLOWs-app software hasdemonstrated in a wide range of applications its great capability to developreliable data-driven hybrid ROMs, highlighting its potential in understandingcomplex non-linear dynamical systems and offering valuable insights intovarious applications. This article presents the mathematical background, reviewsome examples of applications and introduces a short tutorial ofModelFLOWs-app.

ModelFLOWs-app: data-driven post-processing and reduced order modelling tools