mlsanityData to 7 Oct 2026

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

arXiv 1907.09909 ↗First posted 23 Jul 2019math.NA cs.CE cs.NAcited by 21

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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).

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2019.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 7; 2024: 4; 2025: 3.

21 papers.

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  1. 2023β-Variational autoencoders and transformers for reduced-order modelling of fluid flowscited by 76
  2. 2024Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulationscited by 24
  3. 2023Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networkscited by 17
  4. 2021The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equationscited by 14
  5. 2022A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterizationcited by 8
  6. 2023CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boostingcited by 7
  7. 2023Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directionscited by 7
  8. 2021Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximationcited by 6
  9. 2023A hybrid Decoder-DeepONet operator regression framework for unaligned observation datacited by 6
  10. 2024CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilationcited by 6
  11. 2025Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Networkcited by 6
  12. 2022Physics Guided Machine Learning for Variational Multiscale Reduced Order Modelingcited by 4
9 more
  1. 2021Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systemscited by 3
  2. 2024Minimum Reduced-Order Models via Causal Inferencecited by 2
  3. 2024Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket enginecited by 2
  4. 2025Autoencoder-based non-intrusive model order reduction in continuum mechanicscited by 2
  5. 2021Hybrid neural network reduced order modelling for turbulent flows with geometric parameterscited by 1
  6. 2023A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problemscited by 1
  7. 2023Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jetscited by 1
  8. 2025Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)cited by 1
  9. 2021Paradigm Shift Through the Integration of Physical Methodology and Data Sciencecited by 0