Summary of Applications Of Scientific Machine Learning For the Analysis Of Functionally Graded Porous Beams, by Mohammad Sadegh Eshaghi et al.
Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams
by Mohammad Sadegh Eshaghi, Mostafa Bamdad, Cosmin Anitescu, Yizheng Wang, Xiaoying Zhuang, Timon Rabczuk
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper explores various Scientific Machine Learning (SciML) approaches for analyzing functionally graded porous beams, which have varying material properties. The methods use neural networks to approximate displacement fields and derive equations governing beam behavior based on the continuum formulation. Three approaches are considered: Physics-Informed Neural Network (PINN), Deep Energy Method (DEM), and Neural Operator methods. A neural operator is trained to predict the response of the porous beam with functionally graded material under any porosity distribution pattern or traction condition. The results are validated against analytical and numerical reference solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study investigates different ways to use artificial intelligence for analyzing special types of beams that have varying properties. It looks at how well three different methods work in predicting what these beams will do when they’re stretched or compressed. The methods use a type of neural network called a Physics-Informed Neural Network, another one called the Deep Energy Method, and a third one that’s based on data. The results are checked against some known answers to make sure they’re accurate. |
Keywords
» Artificial intelligence » Machine learning » Neural network
