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Graph Network-based Simulators

GNS

21 papers tagged archive 2025-07-28

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.

PaperSource

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.

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.

TaskPapers
Graph Neural Network6
Benchmarking2
Computational Efficiency2
Deep Reinforcement Learning2
Fairness2
Scheduling2
CPU1
Friction1
GPU1
Image Classification1
Language Modeling1
Language Modelling1
Meta-Learning1
Pose Estimation1
Prediction1
Quantization1
Relational Reasoning1
Sand1
Stochastic Optimization1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with GNS: 2020 to 2024, peak 8 8 0 2020: 1 paper 2020 2021: 4 papers 2021 2022: 4 papers 2022 2023: 4 papers 2023 2024: 8 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (21 dated). Bars are counts, not a trend claim.

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

Graph Models

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