Summary of Deepfea: Deep Learning For Prediction Of Transient Finite Element Analysis Solutions, by Georgios Triantafyllou et al.
DeepFEA: Deep Learning for Prediction of Transient Finite Element Analysis Solutions
by Georgios Triantafyllou, Panagiotis G. Kalozoumis, George Dimas, Dimitris K. Iakovidis
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 Deep learning-based framework, DeepFEA, leverages ConvLSTM network to predict solutions for both nodes and elements of transient finite element analysis (FEA) models. Optimized using Node-Element Loss Optimization (NELO), the proposed framework achieves less than 3% normalized mean and root mean squared error for 2D and 3D simulation scenarios. The DeepFEA method is significantly faster, with inference times two orders of magnitude faster than traditional FEA. This advancement addresses limitations in developing surrogates for transient FEA models, enabling accurate and efficient predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study proposes a deep learning-based framework called DeepFEA to predict solutions for both nodes and elements of finite element analysis (FEA) models. The method uses a ConvLSTM network optimized by Node-Element Loss Optimization (NELO). Results show that DeepFEA can accurately predict FEA simulations with less than 3% error, while being two orders of magnitude faster than traditional FEA. |
Keywords
» Artificial intelligence » Deep learning » Inference » Optimization
