Summary of Enhancing Multiscale Simulations with Constitutive Relations-aware Deep Operator Networks, by Hamidreza Eivazi et al.
Enhancing Multiscale Simulations with Constitutive Relations-Aware Deep Operator Networks
by Hamidreza Eivazi, Mahyar Alikhani, Jendrik-Alexander Tröger, Stefan Wittek, Stefan Hartmann, Andreas Rausch
First submitted to arxiv on: 22 May 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 A hybrid method is proposed for solving multiscale problems in physics and engineering, combining deep operator networks (DeepONets) with numerical homogenization. The approach utilizes neural operators to surrogate model microscale physics, embedding constitutive relations into the model architecture. This allows predicting microscale strains and stresses based on macroscale strain inputs. Numerical homogenization is then used to obtain macroscale quantities of interest. The proposed method is applied to quasi-static solid mechanics problems, demonstrating accurate solutions even with a restricted dataset during model development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve big problems that involve many different scales has been discovered. This problem-solving technique combines two powerful tools: deep learning and numerical methods. It uses special kinds of artificial intelligence (AI) called DeepONets to understand how tiny things behave, and then uses this knowledge to predict what will happen at a larger scale. The method is tested on problems involving the behavior of solids under different types of stress, and it works well even when there isn’t much data to train the AI model. |
Keywords
» Artificial intelligence » Deep learning » Embedding
