Summary of Physics-informed Deeponet with Stiffness-based Loss Functions For Structural Response Prediction, by Bilal Ahmed et al.
Physics-informed DeepONet with stiffness-based loss functions for structural response prediction
by Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda, Waleed El-Sekelly, Borja Garcia de Soto, Tarek Abdoun, Mostafa E. Mobasher
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper introduces a novel approach to finite element modeling using DeepOnet, a physics-informed network driven by structural balance laws. The method enables real-time prediction of structural static responses, eliminating the need for extensive pre-processing and analysis. The trained model can generate solutions for entire domains within seconds, making it suitable for various load classes and magnitudes. The paper demonstrates the effectiveness of this approach on two structures: a simple 2D beam and a comprehensive 3D bridge model. To predict multiple variables, the authors utilize split branch/trunk and combined DeepONets, achieving an error rate of less than 5% with reduced training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to improve how we analyze big buildings and bridges. Right now, it takes a lot of time and effort to make predictions about how these structures will behave under different loads. The researchers created a new way to do this using something called DeepOnet, which is like a super smart computer program that can learn from data. They tested their method on two different structures and found that it was really accurate and fast! This could be very helpful for people who design and build big buildings and bridges. |
