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

arXiv: https://arxiv.org/abs/2305.17150 · HTML: https://mlsanity.com/paper/2305.17150/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2023.

## Built on by (most cited first, 5 of 5)

- [Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets](https://mlsanity.com/paper/2404.19579/) (2024)
- [LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements](https://mlsanity.com/paper/2411.17433/) (2024)
- [Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography Databases](https://mlsanity.com/paper/2504.07606/) (2025)
- [A temporal deep learning framework for calibration of low-cost air quality sensors](https://mlsanity.com/paper/2604.21527/) (2026)
- [Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications](https://mlsanity.com/paper/2606.24989/) (2026)

## Abstract (arXiv)

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.