Pith Number

pith:4X2OE44C

pith:2025:4X2OE44CQT6NZBCKWTSLATHQXY

not attested not anchored not stored refs pending

TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion

Brian Sammuli, Futian Zhang, Lawson Fuller, Orso Meneghini, Raffi Nazikian, Rose Yu, Sterling Smith, Tom Neiser, Wesley Liu, Yadi Cao

TGLF-WINN achieves full accuracy of turbulent transport predictions using only 25% of the training data through Bayesian active learning and physics-guided regularization.

arxiv:2509.07024 v3 · 2025-09-07 · physics.plasm-ph · cs.LG

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3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

Adding Bayesian Active Learning, TGLF-WINN matches TGLF-NN's full-data offline accuracy using only 25% of the training data, within 2.8% of TGLF-NN's full-data baseline and 4.3% of our own full-data result.

C2weakest assumption

The training dataset (even when subsampled) is statistically representative of the full range of plasma conditions encountered in whole-device simulations, so that uncertainty estimates from the Bayesian model reliably guide selection of informative points without missing important regimes.

C3one line summary

TGLF-WINN matches standard neural network surrogate accuracy for TGLF transport fluxes using only 25% of the training data via feature tuning, physics-guided wavenumber regularization, and Bayesian active learning.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T01:04:57.105558Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e5f4e2738284fcdc844ab4e4b04cf0be011500ee1aae9be1a6042c6784d5af81

Aliases

arxiv: 2509.07024 · arxiv_version: 2509.07024v3 · doi: 10.48550/arxiv.2509.07024 · pith_short_12: 4X2OE44CQT6N · pith_short_16: 4X2OE44CQT6NZBCK · pith_short_8: 4X2OE44C

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4X2OE44CQT6NZBCKWTSLATHQXY \
  | 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: e5f4e2738284fcdc844ab4e4b04cf0be011500ee1aae9be1a6042c6784d5af81

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a0b9c1ae3905c3cf97d694a016cd748d1cfdb0bd79100dac3c3ee7b6713021d6",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.plasm-ph",
    "submitted_at": "2025-09-07T09:36:51Z",
    "title_canon_sha256": "07b7416266227bdabb642c12b230892d905f2f2febb632e756b8545e6e20bd7a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2509.07024",
    "kind": "arxiv",
    "version": 3
  }
}