Summary of Combining Machine Learning with Computational Fluid Dynamics Using Openfoam and Smartsim, by Tomislav Maric and Mohammed Elwardi Fadeli and Alessandro Rigazzi and Andrew Shao and Andre Weiner
Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
by Tomislav Maric, Mohammed Elwardi Fadeli, Alessandro Rigazzi, Andrew Shao, Andre Weiner
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Fluid Dynamics (physics.flu-dyn)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning (ML) and computational fluid dynamics (CFD) are combined to enhance simulations of complex systems in this research paper. The integration of these two fields offers promising solutions for various technical and natural phenomena. However, the development of CFD+ML algorithms is hindered by data exchange, synchronization, and calculation issues on heterogeneous hardware, particularly when dealing with large-scale problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are joining forces between machine learning and computational fluid dynamics to create better simulations of complicated systems. This helps us understand and predict things like how fluids move or how buildings behave in strong winds. But making these special algorithms work is very difficult because they need to talk to each other and share data, which can be a challenge when working with big problems. |
Keywords
* Artificial intelligence * Machine learning
