Pith Number

pith:CNGYA4L5

pith:2026:CNGYA4L55TUN3WEIF7ZXFWUS52

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Dynamical scaling method improved by a deep learning approach

Yukiyasu Ozeki, Yusuke Terasawa

A neural network estimates scaling parameters from full dynamical datasets at lower cost than Gaussian process regression.

arxiv:2603.06008 v2 · 2026-03-06 · cond-mat.stat-mech

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Claims

C1strongest claim

The neural network method significantly reduces the computational cost and enables the use of the entire dataset, achieving higher accuracy and computational efficiency than conventional Gaussian process regression approaches for dynamical scaling analysis in the 2D Ising and 3-state Potts models.

C2weakest assumption

That a neural network can be trained to estimate scaling parameters from large dynamical scaling datasets with sufficient accuracy and without introducing systematic biases that would offset the claimed gains in efficiency and precision.

C3one line summary

A neural network replaces Gaussian process regression in dynamical scaling analysis, cutting computational cost while improving accuracy on 2D Ising and Potts models.

References

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[1] In this approach, we construct the scal- ing function Φ(·), as expressed in Eq

[2] In the dynamical scaling analy- sis of second-order transitions, the relaxation timeτ(T) exhibits a characteristic critical behavior, and the data must be transformed accordingly 2000

[3] Cardy:Finite-size Scaling(Current physics 1988

[4] Y. Ozeki and N. Ito: J. Phys. A: Math. Theor.40(2007) R149 2007

[5] Y. Ozeki, S. Yotsuyanagi, T. Sakai, and Y. Echinaka: Phys. Rev. E89(2014) 022122 2014

Formal links

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Receipt and verification

First computed	2026-05-20T00:00:35.793350Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

134d80717dece8ddd8882ff372da92ee89540cee7644962c2669d1a941d6ebc7

Aliases

arxiv: 2603.06008 · arxiv_version: 2603.06008v2 · doi: 10.48550/arxiv.2603.06008 · pith_short_12: CNGYA4L55TUN · pith_short_16: CNGYA4L55TUN3WEI · pith_short_8: CNGYA4L5

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CNGYA4L55TUN3WEIF7ZXFWUS52 \
  | 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: 134d80717dece8ddd8882ff372da92ee89540cee7644962c2669d1a941d6ebc7

Canonical record JSON

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    "primary_cat": "cond-mat.stat-mech",
    "submitted_at": "2026-03-06T08:05:46Z",
    "title_canon_sha256": "a75b7057952a98121df58fb8155a407026e04b9d68bfb32db4c829fc7980ed94"
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