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Reliability of Probabilistic Emulation of Physical Systems

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arXiv:2606. 12997v1 Announce Type: cross Abstract: Two dominant approaches have emerged for generating probabilistic forecasts of physical systems: generative models, such as diffusion or flow matching; and ensembles of deterministic models with stochasticity injected, trained using the continuous ranked probability score (CRPS) loss.

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Authors: Sam F. Greenbury (The Alan Turing Institute), Radka Jersakova (The Alan Turing Institute), Paolo Conti (The Alan Turing Institute, Autodesk Research), Marjan Famili (The Alan Turing Institute, PhysicsX), Christopher Iliffe Sprague (The Alan Turing Institute, Orbital), Edwin Brown (The Alan Turing Institute, University of Sheffield), Jason D. McEwen (The Alan Turing Institute, University College London)
Released: 2026-06-12 (27d ago)
Topic: Related to current project
Relation to my work: Read next because Reliability of Probabilistic Emulation of Physical Systems overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, under, good, eval, rate, implement, trained, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.12997

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