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A later PyTorch re-implementation README warns accuracy may degrade over time and extrapolation in time should be avoided.","status":"active","body":{"text":"Both papers report plausible rollouts far longer than the training trajectories (GNS: thousands of steps; MGN: 40,000 steps from a model trained on 400-step trajectories) after training on one-step targets, and GNS credits input-noise injection for this. Generalisation claims are specific: GNS to about 10x more particles and unseen shapes, MGN to a 20k-node windsock and wider angle/speed ranges with RMSE 11.5 to 12.4/13.1. Failures are also stated: GNS rigid block deformation in vigorous shaking and a stuck-goop bad seed; MGN cloth decoherence after about 50 steps; a baseline GNS without mesh-space features is unstable on cloth. A later PyTorch re-implementation README warns accuracy may degrade over time and extrapolation in time should be avoided.\n\nCurator qualifications: All excerpts located; 'stable for thousands of steps' vs cloth decoherence after ~50 steps (plausibility vs positional agreement) kept.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","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/NVIDIA/physicsnemo/tree/86f7bd46e393c04e38977033c283b899647e0e6d","note":"candidate evidence excerpts"}]},"categories":["T07","T11","T15"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2002.09405v2","claim":"Both papers report plausible rollouts far longer than the training trajectories (GNS: thousands of steps; MGN: 40,000 steps from a model trained on 400-step trajectories) after training on one-step targets, and GNS credits input-noise injection for this. Generalisation claims are specific: GNS to about 10x more particles and unseen shapes, MGN to a 20k-node windsock and wider angle/speed ranges with RMSE 11.5 to 12.4/13.1. Failures are also stated: GNS rigid block deformation in vigorous shaking and a stuck-goop bad seed; MGN cloth decoherence after about 50 steps; a baseline GNS without mesh-space features is unstable on cloth. 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Generalisation claims are specific: GNS to about 10x more particles and unseen shapes, MGN to a 20k-node windsock and wider angle/speed ranges with RMSE 11.5 to 12.4/13.1. Failures are also stated: GNS rigid block deformation in vigorous shaking and a stuck-goop bad seed; MGN cloth decoherence after about 50 steps; a baseline GNS without mesh-space features is unstable on cloth. A later PyTorch re-implementation README warns accuracy may degrade over time and extrapolation in time should be avoided.\n\nCurator qualifications: All excerpts located; 'stable for thousands of steps' vs cloth decoherence after ~50 steps (plausibility vs positional agreement) kept.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","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/NVIDIA/physicsnemo/tree/86f7bd46e393c04e38977033c283b899647e0e6d","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Do learned physics simulators stay stable over long rollouts and generalise beyond their training data, and what do the sources say breaks?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Both papers report plausible rollouts far longer than the training trajectories (GNS: thousands of steps; MGN: 40,000 steps from a model trained on 400-step trajectories) after training on one-step targets, and GNS credits input-noise injection for this. 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