Combining machine learning with computational fluid dynamics using OpenFOAM and SmartSim

Research output: Contribution to journalResearch articleContributedpeer-review


  • Tomislav Maric - , Technische Universität Darmstadt (Author)
  • Mohammed Elwardi Fadeli - , Technische Universität Darmstadt (Author)
  • Alessandro Rigazzi - , Hewlett Packard Enterprise (Author)
  • Andrew Shao - , Hewlett Packard Enterprise (Author)
  • Andre Weiner - , Chair of Fluid Mechanics (Author)


Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems. However, CFD+ML algorithms require exchange of data, synchronization, and calculation on heterogeneous hardware, making their implementation for large-scale problems exceptionally challenging. We provide an effective and scalable solution to developing CFD+ML algorithms using open source software OpenFOAM and SmartSim. SmartSim provides an Orchestrator that significantly simplifies the programming of CFD+ML algorithms enables scalable data exchange between ML and CFD clients. We show how to leverage SmartSim to effectively couple different segments of OpenFOAM with ML, including pre/post-processing applications, function objects, and mesh motion solvers. We additionally provide an OpenFOAM sub-module with examples that can be used as starting points for real-world applications in CFD+ML.


Original languageEnglish
Publication statusPublished - 2024

External IDs

RIS Maric2024
Scopus 85190780555



  • Computational Fluid Dynamics, Machine Learning, Workflow