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?
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.
Structured fields
{
"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
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |