# StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction

arXiv: https://arxiv.org/abs/2006.11376 · HTML: https://mlsanity.com/paper/2006.11376/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 5 of 5)

- [Deep Generative Models in Engineering Design: A Review](https://mlsanity.com/paper/2110.10863/) (2021)
- [Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process](https://mlsanity.com/paper/2212.05885/) (2022)
- [SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field with Attention Mechanism and Perceptual Features](https://mlsanity.com/paper/2104.09276/) (2021)
- [Development of a deep learning platform for optimising sheet stamping geometries subject to manufacturing constraints](https://mlsanity.com/paper/2202.03422/) (2022)
- [Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net](https://mlsanity.com/paper/2206.01122/) (2022)

## Abstract (arXiv)

Using deep learning to analyze mechanical stress distributions has been gaining interest with the demand for fast stress analysis methods. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physics without prior knowledge of underlying equations. However, most studies restrict the variation of geometry or boundary conditions, making these methods difficult to be generalized to unseen configurations. We propose a conditional generative adversarial network (cGAN) model for predicting 2D von Mises stress distributions in solid structures. The cGAN learns to generate stress distributions conditioned by geometries, load, and boundary conditions through a two-player minimax game between two neural networks with no prior knowledge. By evaluating the generative network on two stress distribution datasets under multiple metrics, we demonstrate that our model can predict more accurate high-resolution stress distributions than a baseline convolutional neural network model, given various and complex cases of geometry, load and boundary conditions.