Methods › Graphs › Graph Models › GNS
Graph Network-based Simulators
GNS
Introduced by Alvaro Sanchez-Gonzalez et al. in Learning to Simulate Complex Physics with Graph Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Graph Network-Based Simulators is a type of graph neural network that represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing.
Papers archive 2025-07-28
21 shown of 21, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers 1 Nov 2024 · 1 repository · arXiv:2411.00999Syntology ran 2 of 13 samples · 11 unverified · 13 pointer-only (licence)
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MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks Simulators 4 Oct 2024 · 1 repository · arXiv:2410.03107
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UAV-Enabled Data Collection for IoT Networks via Rainbow Learning 22 Sep 2024 · 0 repositories · arXiv:2409.14521
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Learning to Simulate Aerosol Dynamics with Graph Neural Networks 20 Sep 2024 · 1 repository · arXiv:2409.13861
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Finite-difference-informed graph network for solving steady-state incompressible flows on block-structured grids 15 Jun 2024 · 0 repositories · arXiv:2406.10534
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Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization 12 Jun 2024 · 0 repositories · arXiv:2406.08001
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Adaptive Graph Normalized Sign Algorithm 7 May 2024 · 0 repositories · arXiv:2405.04107
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Inverse analysis of granular flows using differentiable graph neural network simulator 17 Jan 2024 · 0 repositories · arXiv:2401.13695
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Three-dimensional granular flow simulation using graph neural network-based learned simulator 13 Nov 2023 · 0 repositories · arXiv:2311.07416
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LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite 28 Sep 2023 · 2 repositories · arXiv:2309.16342Syntology ran 23 of 27 samples · 4 unverified · 3 pointer-only (licence)
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Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problems 23 Sep 2023 · 0 repositories · arXiv:2309.13348
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Graph Neural Network-based surrogate model for granular flows 9 May 2023 · 1 repository · arXiv:2305.05218
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GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling 18 Nov 2022 · 1 repository · arXiv:2211.10228
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Cost-Efficient Deployment of a Reliable Multi-UAV Unmanned Aerial System 30 Aug 2022 · 0 repositories · arXiv:2208.14503
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Fairness Based Energy-Efficient 3D Path Planning of a Portable Access Point: A Deep Reinforcement Learning Approach 10 Aug 2022 · 0 repositories · arXiv:2208.05265
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Minority Report: A Graph Network Oracle for In Situ Visualization 25 Jun 2022 · 0 repositories · arXiv:2206.12683
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Subspace Graph Physics: Real-Time Rigid Body-Driven Granular Flow Simulation 18 Nov 2021 · 1 repository · arXiv:2111.10206
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Manipulation of Granular Materials by Learning Particle Interactions 3 Nov 2021 · 1 repository · arXiv:2111.02274
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Learning to Schedule Learning rate with Graph Neural Networks 29 Sep 2021 · 0 repositories
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Relational VAE: A Continuous Latent Variable Model for Graph Structured Data 30 Jun 2021 · 1 repository · arXiv:2106.16049
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Learning to Simulate Complex Physics with Graph Networks 21 Feb 2020 · 13 repositories · arXiv:2002.09405Syntology ran 9 of 24 samples · 15 unverified · 13 pointer-only (licence)
Tasks archive 2025-07-28
20 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections