{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stressgan-a-generative-deep-learning-model","title":"StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction","arxiv_id":"2006.11376","date":"2020-05-30","proceeding":null,"authors":["Jiang Haoliang","Nie Zhenguo","Yeo Roselyn","Farimani Amir Barati","Kara Levent Burak"],"abstract":"Using deep learning to analyze mechanical stress distributions has been\ngaining interest with the demand for fast stress analysis methods. Deep\nlearning approaches have achieved excellent outcomes when utilized to speed up\nstress computation and learn the physics without prior knowledge of underlying\nequations. However, most studies restrict the variation of geometry or boundary\nconditions, making these methods difficult to be generalized to unseen\nconfigurations. We propose a conditional generative adversarial network (cGAN)\nmodel for predicting 2D von Mises stress distributions in solid structures. The\ncGAN learns to generate stress distributions conditioned by geometries, load,\nand boundary conditions through a two-player minimax game between two neural\nnetworks with no prior knowledge. By evaluating the generative network on two\nstress distribution datasets under multiple metrics, we demonstrate that our\nmodel can predict more accurate high-resolution stress distributions than a\nbaseline convolutional neural network model, given various and complex cases of\ngeometry, load and boundary conditions.","url_abs":"http://arxiv.org/abs/2006.11376v1","url_pdf":"http://arxiv.org/pdf/2006.11376v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stressgan-a-generative-deep-learning-model","repo_url":"https://github.com/zhenguonie/2020_StressGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}