Cover Image

XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Baral, Shilaj, Lee, Youngkyu, Khanal, Sangam, Jeon, Joongoo
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Fluid Dynamics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets
by: Khanal, Sangam, et al.
Published: (2025)

Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
by: Lee, Minseo, et al.
Published: (2026)

Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
by: Jeon, Joongoo, et al.
Published: (2024)

On the development of OpenFOAM solvers for simulating MHD micropolar fluid flows with or without the effect of micromagnetorotation
by: Aslani, Kyriaki-Evangelia, et al.
Published: (2025)

Assessment of modern shock capturing schemes for all-speed flows in the OpenFOAM framework
by: Ray, Anurag Adityanarayan, et al.
Published: (2025)

Implementation and verification of the resolved Reynolds stress transport equations in OpenFOAM
by: Rincón, Mario J., et al.
Published: (2025)

Wall-modeled large-eddy simulation of turbulent smooth body separation using the OpenFOAM flow solver
by: Hansen, Christoffer, et al.
Published: (2024)

CoronaryHemodynamics: An Automated Simulation Framework for Coronary Artery Hemodynamics Using OpenFOAM
by: Mao, Yijin, et al.
Published: (2025)

upstreamFoam: an OpenFOAM-based solver for heterogeneous porous media at different scales
by: Lange, Roberto, et al.
Published: (2023)

Application of a Pressured-Based OpenFOAM Solver for Rotating Detonation Engines
by: Kwak, Keunjae, et al.
Published: (2025)

Development of low-dissipative projection method framework integrating various high-order time integration schemes using OpenFOAM
by: Guo, Hao, et al.
Published: (2023)

DRLinFluids -- An open-source python platform of coupling Deep Reinforcement Learning and OpenFOAM
by: Wang, Qiulei, et al.
Published: (2022)

A Workflow for Utilizing OpenFOAM Data Structure in Physics-Informed Deep Learning Training
by: Mao, Yijin, et al.
Published: (2024)

An OpenFOAM face-centred solver for incompressible flows robust to mesh distortion
by: Cortellessa, Davide, et al.
Published: (2024)

Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
by: Maric, Tomislav, et al.
Published: (2024)

uniGasFoam: a particle-based OpenFOAM solver for multiscale rarefied gas flows
by: Vasileiadis, Nikos, et al.
Published: (2024)

cfdmfFTFoam: A front-tracking solver for multiphase flows on general unstructured grids in OpenFOAM
by: Amani, Ehsan
Published: (2026)

OpenFOAMGPT: a RAG-Augmented LLM Agent for OpenFOAM-Based Computational Fluid Dynamics
by: Pandey, Sandeep, et al.
Published: (2025)

Characterisation and extension of a rigid body dynamics solver coupled with OpenFOAM for flight performance analysis of flapping-wing drones
by: Poletti, Romain, et al.
Published: (2025)

Scalable CFD Simulations in Multi-Billion Voxel Micro-CT Images of Porous Materials Using OpenFOAM on ARCHER2
by: Maes, J., et al.
Published: (2025)

Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework
by: Gonzalez-Sieiro, Jesus, et al.
Published: (2024)

twoPhaseInterTrackFoam: an OpenFOAM module for Arbitrary Lagrangian/Eulerian Interface Tracking with Surfactants and Subgrid-Scale Modeling
by: Schwarzmeier, Moritz, et al.
Published: (2024)

OpenFOAM computational fluid dynamics (CFD) solver for magnetohydrodynamic open cycles, applied to the Sakhalin pulsed magnetohydrodynamic generator (PMHDG)
by: Marzouk, Osama A.
Published: (2026)

MetaOpenFOAM: an LLM-based multi-agent framework for CFD
by: Chen, Yuxuan, et al.
Published: (2024)

Divergence-aware adaptive prediction framework for accelerating CFD simulations of unsteady flows
by: Zou, Xiangrui, et al.
Published: (2026)

Quantum computing for simulation of fluid dynamics
by: Sanavio, Claudio, et al.
Published: (2024)

Assessment of unsteady flow predictions using hybrid deep learning based reduced order models
by: Bukka, Sandeep Reddy, et al.
Published: (2020)

Data-driven modeling of unsteady flow based on deep operator network
by: Bai, Heming, et al.
Published: (2024)

Simulating unsteady fluid flows on a superconducting quantum processor
by: Meng, Zhaoyuan, et al.
Published: (2024)

U Net LSTM with incremental time-stepping for robust long-horizon unsteady flow prediction
by: Madiega, Blaise, et al.
Published: (2025)

A comparative evaluation of turbulence models for simulation of unsteady cavitating flows
by: Apte, Dhruv, et al.
Published: (2023)

SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
by: Xiao, Maochao, et al.
Published: (2025)

Reduced order modeling of the unsteady pressure on turbine rotor blades using deep learning
by: Joachim, Dominique, et al.
Published: (2025)

Spectral adjoint-based assimilation of sparse data in unsteady simulations of turbulent flows
by: Plogmann, Justin, et al.
Published: (2024)

Reduced-order modeling of unsteady fluid flow using neural network ensembles
by: Halder, Rakesh, et al.
Published: (2024)

Chapter 3 An introduction to OpenFOAM
by: Mohammadian, Abdolmajid, et al.
Published: (2023)

Generative AI for fast and accurate statistical computation of fluids
by: Molinaro, Roberto, et al.
Published: (2024)

OpenFOAMGPT 2.0: end-to-end, trustworthy automation for computational fluid dynamics
by: Feng, Jingsen, et al.
Published: (2025)

Monolithic framework to simulate fluid-structure interaction problems using geometric volume-of-fluid method
by: Prajapati, Soham, et al.
Published: (2025)

Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models
by: Abadía-Heredia, Rodrigo, et al.
Published: (2024)