# Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows

arXiv: https://arxiv.org/abs/1907.09909 · HTML: https://mlsanity.com/paper/1907.09909/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 21 of 21)

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- [A hybrid Decoder-DeepONet operator regression framework for unaligned observation data](https://mlsanity.com/paper/2308.09274/) (2023)
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- [Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network](https://mlsanity.com/paper/2507.08749/) (2025)
- [Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling](https://mlsanity.com/paper/2205.12419/) (2022)
- [Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systems](https://mlsanity.com/paper/2110.01929/) (2021)
- [Minimum Reduced-Order Models via Causal Inference](https://mlsanity.com/paper/2407.00271/) (2024)
- [Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine](https://mlsanity.com/paper/2407.09994/) (2024)
- [Autoencoder-based non-intrusive model order reduction in continuum mechanics](https://mlsanity.com/paper/2509.02237/) (2025)
- [Hybrid neural network reduced order modelling for turbulent flows with geometric parameters](https://mlsanity.com/paper/2107.09591/) (2021)
- [A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems](https://mlsanity.com/paper/2301.09926/) (2023)
- [Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets](https://mlsanity.com/paper/2309.13955/) (2023)
- [Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)](https://mlsanity.com/paper/2502.04685/) (2025)
- [Paradigm Shift Through the Integration of Physical Methodology and Data Science](https://mlsanity.com/paper/2110.01408/) (2021)

## Abstract (arXiv)

In this work we present a Reduced Order Model which is specifically designed to deal with turbulent flows in a finite volume setting. The method used to build the reduced order model is based on the idea of merging/combining projection-based techniques with data-driven reduction strategies. In particular, the work presents a mixed strategy that exploits a data-driven reduction method to approximate the eddy viscosity solution manifold and a classical POD-Galerkin projection approach for the velocity and the pressure fields, respectively. The newly proposed reduced order model has been validated on benchmark test cases in both steady and unsteady settings with Reynolds up to Re=O(10^5).