Pith Number

pith:QBMGZVLJ

pith:2026:QBMGZVLJRKTCKBJSLFS2KMBQTT

not attested not anchored not stored refs pending

Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability

Akzhol Almukhametov, Doyeong Lim, Rui Hu, Yang Liu

A graph neural network with neural ODE dynamics forecasts reactor thermal-hydraulic states accurately at locations without sensors and adapts to real data.

arxiv:2604.07292 v2 · 2026-04-08 · cs.LG

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\pithnumber{QBMGZVLJRKTCKBJSLFS2KMBQTT}

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2 Internet Archive

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4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

The GNN-ODE surrogate achieves an average MAE of 0.91 K at 60 s and 2.18 K at 300 s for uninstrumented nodes, with R² up to 0.995 for missing-node state reconstruction; after fine-tuning on 30 experimental sequences the learned flow-dependent heat-transfer scaling recovers a Reynolds-number exponent consistent with established correlations.

C2weakest assumption

That the directed sensor graph and topology-guided initializer faithfully encode the true hydraulic connectivity and that the physics-informed message passing plus Neural ODE can generalize from simulation transients to real experimental data without introducing systematic bias in the recovered constitutive relation.

C3one line summary

A GNN-ODE digital twin forecasts reactor thermal-hydraulic states under partial observability, achieving low error on held-out transients and recovering a physical heat-transfer correlation during sim-to-real adaptation.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:05:44.438655Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

80586cd5698aa62505325965a530309cc9c84e447eab5f4ba510b0f76284e10d

Aliases

arxiv: 2604.07292 · arxiv_version: 2604.07292v2 · doi: 10.48550/arxiv.2604.07292 · pith_short_12: QBMGZVLJRKTC · pith_short_16: QBMGZVLJRKTCKBJS · pith_short_8: QBMGZVLJ

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT \
  | 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: 80586cd5698aa62505325965a530309cc9c84e447eab5f4ba510b0f76284e10d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "69d29eee70488b4e32d30ec4203eecec2a9b9b5788172c37d0aaebc510d8300a",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-08T16:58:14Z",
    "title_canon_sha256": "cd9cae3d701855c1ac719452693f9f94b138c3cd8136f67a7908df31cac8e76f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.07292",
    "kind": "arxiv",
    "version": 2
  }
}