  This article presents an innovative open-source software named
ModelFLOWs-app, written in Python, which has been created and tested to
generate precise and robust hybrid reduced order models (ROMs) fully
data-driven. By integrating modal decomposition and deep learning methods in
diverse ways, the software uncovers the fundamental patterns in dynamic
systems. This acquired knowledge is then employed to enrich the comprehension
of the underlying physics, reconstruct databases from limited measurements, and
forecast the progression of system dynamics. These hybrid models combine
experimental and numerical database, and serve as accurate alternatives to
numerical simulations, effectively diminishing computational expenses, and also
as tools for optimization and control. The ModelFLOWs-app software has
demonstrated in a wide range of applications its great capability to develop
reliable data-driven hybrid ROMs, highlighting its potential in understanding
complex non-linear dynamical systems and offering valuable insights into
various applications. This article presents the mathematical background, review
some examples of applications and introduces a short tutorial of
ModelFLOWs-app.
