ExampleGuidancev1.0.0

Weather simulation where machine learning approximates physical processes

A meteorological institute runs physics based weather models and uses machine learning to approximate complex atmospheric processes such as cloud microphysics and turbulence. The estimated values are then fed into the established physics model, which produces the actual forecast.

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Address and citation

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Identifier: praxikon:eu:ai-act:example:example-fysicasimulatie-met-ml-versnelling
Payload hash (sha256): fd65201978e0fb6b88a6604f62c5e0f0731358ce5fd8bcd66fa88bb26de4beb5

Citation line

Praxikon, "Weather simulation where machine learning approximates physical processes", praxikon:eu:ai-act:example:example-fysicasimulatie-met-ml-versnelling@1.0.0, dataset praxikon:sys:registry:dataset:ai-act-implementation-graph 2.1.0 (schema 1.4.0), effective_at 2026-08-08T00:00:00.000Z, known_at 2026-08-14T00:00:00.000Z, sha256 fd65201978e0fb6b88a6604f62c5e0f0731358ce5fd8bcd66fa88bb26de4beb5

Version: 1.0.0
Legal time (effective_at): 8 August 2026
Knowledge time (known_at): 8 August 2026
Closed on: Not closed
Topics: examples

Review status: Placed against the official source (8 August 2026). Next check due by 4 February 2027. The check date is the knowledge date of this version; no later recheck has been recorded.

What this object links to

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The obligation this hangs off

1 of 1 shown

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Source
Official fact on this object, with its locator.
- Guidelines on the definition of an AI system, C(2025) 5053 final
  Locator: Commission Guidelines C(2025) 5053 final, 29.7.2025, borderline cases under the definition in Article 3(1)
  praxikon:eu:ai-act:source:commission-ai-system-definition-guidelines
  Open official source
Via
No condition or exception recorded on this object.
Consequence
- ObligationArticle 4: AI literacy
  praxikon:eu:ai-act:obligation:article-4-ai-literacy

What this object is about

1 of 1 shown

The object is about this role. Undifferentiated: it does not follow that the duty rests on this role.

Source
Official fact on this object, with its locator.
- Guidelines on the definition of an AI system, C(2025) 5053 final
  Locator: Commission Guidelines C(2025) 5053 final, 29.7.2025, borderline cases under the definition in Article 3(1)
  praxikon:eu:ai-act:source:commission-ai-system-definition-guidelines
  Open official source
Via
No condition or exception recorded on this object.
Consequence
- ActorProvider of an AI system
  praxikon:eu:ai-act:actor:provider

What this object states

Official fact

Attributable to a named primary source, with a locator. Where they differ, the official source prevails.

The Commission guidelines on the definition of an AI system use this case to draw the line between software that does and does not fall under the regulation. The document is non-binding.
- Locator: Commission Guidelines C(2025) 5053 final, 29.7.2025, borderline cases under the definition in Article 3(1)praxikon:eu:ai-act:source:commission-ai-system-definition-guidelinesOpen official source

Our interpretation

Our own reading. It can change without the law changing, and it is not the position of a supervisory authority.

That your model can infer from input does not by itself bring it within the definition, since the guidelines justify excluding such accelerating systems precisely because they do not transcend basic data processing.
- Locator: Commission Guidelines C(2025) 5053 final, 29.7.2025, borderline cases under the definition in Article 3(1)praxikon:eu:ai-act:source:commission-ai-system-definition-guidelinesOpen official source

When this applies

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When this does not apply

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Referring to this object

Citation block

Copy this reference into your advice, article or file. The identifier, the version and the hash keep the statement findable later, even once the dataset has moved on.

Reference

Praxikon, "Weather simulation where machine learning approximates physical processes",
praxikon:eu:ai-act:example:example-fysicasimulatie-met-ml-versnelling@1.0.0,
dataset praxikon:sys:registry:dataset:ai-act-implementation-graph 2.1.0 (schema 1.4.0),
effective_at 2026-08-08T00:00:00.000Z, known_at 2026-08-08T00:00:00.000Z,
sha256 fd65201978e0fb6b88a6604f62c5e0f0731358ce5fd8bcd66fa88bb26de4beb5,
https://www.praxikon.com/en/verkenner/example/example-fysicasimulatie-met-ml-versnelling
(https://www.praxikon.com/api/v1/entities?id=praxikon%3Aeu%3Aai-act%3Aexample%3Aexample-fysicasimulatie-met-ml-versnelling&effective_at=2026-08-08&known_at=2026-08-08&lang=en, accessed 2026-08-25)

Short form

praxikon:eu:ai-act:example:example-fysicasimulatie-met-ml-versnelling@1.0.0 (sha256 fd652019)

BibTeX

@misc{praxikon-eu-ai-act-example-example-fysicasimulatie-met-ml-versnelling-1-0-0,
  author       = {{Praxikon}},
  title        = {Weather simulation where machine learning approximates physical processes},
  year         = {2026},
  version      = {1.0.0},
  number       = {praxikon:eu:ai-act:example:example-fysicasimulatie-met-ml-versnelling},
  howpublished = {AI Act Change \& Evidence Graph, dataset 2.1.0, schema 1.4.0},
  note         = {effective_at 2026-08-08T00:00:00.000Z; known_at 2026-08-08T00:00:00.000Z; sha256 fd65201978e0fb6b88a6604f62c5e0f0731358ce5fd8bcd66fa88bb26de4beb5},
  url          = {https://www.praxikon.com/en/verkenner/example/example-fysicasimulatie-met-ml-versnelling},
  urldate      = {2026-08-25},
  language     = {en}
}

CSL JSON

[
  {
    "id": "praxikon:eu:ai-act:example:example-fysicasimulatie-met-ml-versnelling@1.0.0",
    "type": "dataset",
    "title": "Weather simulation where machine learning approximates physical processes",
    "container-title": "AI Act Change & Evidence Graph",
    "publisher": "Praxikon",
    "version": "1.0.0",
    "number": "praxikon:eu:ai-act:example:example-fysicasimulatie-met-ml-versnelling",
    "URL": "https://www.praxikon.com/en/verkenner/example/example-fysicasimulatie-met-ml-versnelling",
    "language": "en",
    "issued": {
      "date-parts": [
        [
          2026,
          8,
          8
        ]
      ]
    },
    "accessed": {
      "date-parts": [
        [
          2026,
          8,
          25
        ]
      ]
    },
    "note": "dataset praxikon:sys:registry:dataset:ai-act-implementation-graph 2.1.0; schema 1.4.0; effective_at 2026-08-08T00:00:00.000Z; known_at 2026-08-08T00:00:00.000Z; sha256 fd65201978e0fb6b88a6604f62c5e0f0731358ce5fd8bcd66fa88bb26de4beb5; retrieved_from https://www.praxikon.com/api/v1/entities?id=praxikon%3Aeu%3Aai-act%3Aexample%3Aexample-fysicasimulatie-met-ml-versnelling&effective_at=2026-08-08&known_at=2026-08-08&lang=en; licence https://www.praxikon.com/nl/legal/terms"
  }
]

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