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      "statement": "# Graph Network Simulators and MeshGraphNets\n\nLearns particle dynamics by graph encode–process–decode message passing, then extends this learned-simulation approach to irregular meshes using mesh-space/world-space edges and a predicted sizing field with a generic remesher.\n\n## Scientific scope\n\nOne simulator-family record with source-specific bylines. GNS first four and MeshGraphNets first three authors explicitly share contribution. Rollout transfer and speed are empirical, not exact conservation, controlled global-error guarantees or universal replacement of numerical solvers. Model inference cost excludes training and producing reference simulations.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAlvaro Sanchez-Gonzalez; Jonathan Godwin; Tobias Pfaff; Rex Ying; Jure Leskovec; Peter W. Battaglia; Meire Fortunato\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Learning to Simulate Complex Physics with Graph Networks](https://arxiv.org/pdf/2002.09405v1)\n- [Learning Mesh-Based Simulation with Graph Networks](https://arxiv.org/pdf/2010.03409v1)\n",
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