{"url":"/method/meshgraphnet","slug":"meshgraphnet","name":"MeshGraphNet","full_name":"MeshGraphNet","full_name_withheld":false,"description_markdown":"**MeshGraphNet** is a framework for learning mesh-based simulations using [graph neural networks](https://paperswithcode.com/methods/category/graph-models). The model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation. The model uses an Encode-Process-Decode architecture trained with one-step supervision, and can be applied iteratively to generate long trajectories at inference time. The encoder transforms the input mesh $M^{t}$ into a graph, adding extra world-space edges. The processor performs several rounds of message passing along mesh edges and world edges, updating all node and edge embeddings. The decoder extracts the acceleration for each node, which is used to update the mesh to produce $M^{t+1}$.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Learning Mesh-Based Simulation with Graph Networks","paper":"/paper/learning-mesh-based-simulation-with-graph-1","first_author":"Tobias Pfaff","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/learning-mesh-based-simulation-with-graph-1"},"source":{"url":"https://arxiv.org/abs/2010.03409v4","title":"Learning Mesh-Based Simulation with Graph Networks","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Mesh-Based Simulation Models","url":"/methods/category/mesh-based-simulation-models","pwc_aliases":[]},{"area":"Graphs","area_id":"graphs","collection":"Graph Models","url":"/methods/category/graph-models","pwc_aliases":[]}],"n_papers_tagged":5,"archive_num_papers":5,"papers_newest_first":[{"paper":null,"title":"Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows","date":"2025-01-03","arxiv_id":"2501.01934","n_code_links":0,"syntology":null},{"paper":"/paper/x-meshgraphnet-scalable-multi-scale-graph","title":"X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation","date":"2024-11-26","arxiv_id":"2411.17164","n_code_links":1,"syntology":null},{"paper":null,"title":"Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network","date":"2024-11-21","arxiv_id":"2411.14192","n_code_links":0,"syntology":null},{"paper":null,"title":"Learning CO$_2$ plume migration in faulted reservoirs with Graph Neural Networks","date":"2023-06-16","arxiv_id":"2306.09648","n_code_links":0,"syntology":null},{"paper":"/paper/learning-mesh-based-simulation-with-graph-1","title":"Learning Mesh-Based Simulation with Graph Networks","date":"2020-10-07","arxiv_id":"2010.03409","n_code_links":11,"syntology":{"ran":4,"of":4,"unverified":0,"pointer_only":1}}],"papers_shown":5,"tasks":[{"task":"/task/computational-efficiency","name":"Computational Efficiency","papers":1},{"task":"/task/graph-neural-network","name":"Graph Neural Network","papers":1},{"task":"/task/numerical-integration","name":"Numerical Integration","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2020","papers":1},{"year":"2023","papers":1},{"year":"2024","papers":2},{"year":"2025","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/meshgraphnet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}