# What is a 'learned physics simulator' such as DeepMind's GNS or MeshGraphNets, what does it take as input and predict, and what is it trained on?

- id: art_01M5MV0KGKMNYQGG7EQ7QTPABN
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T07, T11, T15 · tags: none
- updated: 2026-10-23T16:55:24.429Z
- url: https://veyrul.com/articles/art_01M5MV0KGKMNYQGG7EQ7QTPABN

In the two DeepMind papers a learned simulator is a graph neural network trained, per scenario family, on trajectories produced by a conventional solver (MPM, SPH, PBD, ArcSim, COMSOL, SU2). It reads the current state (particles or mesh nodes with a short velocity history), predicts per-node acceleration, integrates it with an Euler step, and is applied repeatedly to roll out a trajectory. It is a fast surrogate of the solver it was trained on, not a model that discovers physics; the training supervision is one-step prediction.

In the two DeepMind papers a learned simulator is a graph neural network trained, per scenario family, on trajectories produced by a conventional solver (MPM, SPH, PBD, ArcSim, COMSOL, SU2). It reads the current state (particles or mesh nodes with a short velocity history), predicts per-node acceleration, integrates it with an Euler step, and is applied repeatedly to roll out a trajectory. It is a fast surrogate of the solver it was trained on, not a model that discovers physics; the training supervision is one-step prediction.

Curator qualifications: Excerpted evidence located (one unexcerpted row); GNS/MeshGraphNets inputs/outputs.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2002.09405v2",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/abs/2010.03409v4",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/google-deepmind/deepmind-research/tree/f5de0ede8430809180254ee957abf36ed62579ef",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] In the two DeepMind papers a learned simulator is a graph neural network trained, per scenario family, on trajectories produced by a conventional solver (MPM, SPH, PBD, ArcSim, COMSOL, SU2). It reads the current state (particles or mesh nodes with a short velocity history), predicts per-node acceleration, integrates it with an Euler step, and is applied repeatedly to roll out a trajectory. It is a fast surrogate of the solver it was trained on, not a model that discovers physics; the training supervision is one-step prediction. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2002.09405v2

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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