Pith Number

pith:HVJDYKOI

pith:2025:HVJDYKOIEHSG2VTXXZLBK63S3M

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The Machine Learning Approach to Moment Closure Relations for Plasma: A Review

Enrico Camporeale, Samuel Burles

Machine learning methods are creating closure relations that let fluid plasma models capture kinetic effects.

arxiv:2511.22486 v4 · 2025-11-27 · physics.plasm-ph · cs.LG

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\pithnumber{HVJDYKOIEHSG2VTXXZLBK63S3M}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

This review compiles and analyses the recent surge of machine learning approaches developing improved plasma closure models capable of capturing kinetic phenomena within plasma fluid models.

C2weakest assumption

That the surveyed machine learning methods are representative of the field and that the outlined challenges are the primary barriers to practical use, an assumption that cannot be verified from the abstract alone.

C3one line summary

The review gathers and examines machine learning techniques, including equation discovery and neural network surrogates, for building improved moment closure relations that incorporate kinetic phenomena into plasma fluid models.

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

A Neural Operator Closure for Landau Damping in Electrostatic Plasma

Receipt and verification

First computed	2026-06-19T16:12:49.282251Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3d523c29c821e46d5677be56157b72db3df9c35c255a7101856931d09aad1f34

Aliases

arxiv: 2511.22486 · arxiv_version: 2511.22486v4 · doi: 10.48550/arxiv.2511.22486 · pith_short_12: HVJDYKOIEHSG · pith_short_16: HVJDYKOIEHSG2VTX · pith_short_8: HVJDYKOI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/HVJDYKOIEHSG2VTXXZLBK63S3M \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 3d523c29c821e46d5677be56157b72db3df9c35c255a7101856931d09aad1f34

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b23fbb1eafa3a9870fc57e1ad295a81d12045ee77383df96069d119ba6dd669b",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.plasm-ph",
    "submitted_at": "2025-11-27T14:20:36Z",
    "title_canon_sha256": "98249f45b9c155beb493a57f715244bbb02b55d8af6128e60a1d5dcf55bcc482"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2511.22486",
    "kind": "arxiv",
    "version": 4
  }
}