Summary of Using Parametric Pinns For Predicting Internal and External Turbulent Flows, by Shinjan Ghosh et al.
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
by Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis, Biswadip Dey
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a promising approach to developing parametric surrogate models for simulating turbulent flows using physics-informed neural networks (PINNs). Specifically, it explores the efficacy of the RANS-PINN framework in predicting relevant turbulent flow variables for both internal and external flows. The authors build upon their previous work on predicting flow over a cylinder, introducing a novel sampling approach to ensure training convergence with a complex loss function. This framework has the potential to provide accurate and efficient predictions for various flow problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special computers to simulate how liquids move in different situations. It’s trying to find a way to make these simulations faster and more accurate by using a type of artificial intelligence called neural networks. The goal is to be able to predict what will happen when liquids flow over or through things, like pipes or cylinders. This could help us design better machines and systems that work with liquids. |
Keywords
* Artificial intelligence * Loss function
