{"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/amgnet-multi-scale-graph-neural-networks-for","title":"AMGNET: multi-scale graph neural networks for flow field prediction","arxiv_id":null,"date":"2022-10-13","proceeding":"journal 2022 10","authors":["Zhishuang Yang","Yidao Dong","Xiaogang Deng &Laiping Zhang"],"abstract":"Solving partial differential equations of complex physical systems is a computationally expensive task, especially in Computational Fluid Dynamics(CFD). This drives the application of deep learning methods in solving physical systems. There exist a few deep learning models that are very successful in predicting flow fields of complex physical models, yet most of these still exhibit large errors compared to simulation. Here we introduce AMGNET, a multi-scale graph neural network model based on Encoder-Process-Decoder structure for flow field prediction. Our model employs message passing of graph neural networks at different mesh graph scales. Our method has significantly lower prediction errors than the GCN baseline on several complex fluid prediction tasks, such as airfoil flow and cylinder flow. Our results show that multi-scale representation learning at the graph level is more effective in improving the prediction accuracy of flow field.","url_abs":"https://www.tandfonline.com/doi/full/10.1080/09540091.2022.2131737","url_pdf":"https://www.tandfonline.com/doi/full/10.1080/09540091.2022.2131737","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":"amgnet-multi-scale-graph-neural-networks-for","repo_url":"https://github.com/baoshiaijhin/amgnet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"amgnet-multi-scale-graph-neural-networks-for","repo_url":"https://github.com/PaddlePaddle/PaddleScience","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}