Pith Number

pith:LHV4HDXZ

pith:2026:LHV4HDXZPFVM576DATO4D3VCU2

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Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation

Aditya Sood, Christine Allen-Blanchette, Dongzhe Zheng, Tao Zhong, Yixun Hu

NeFTY recovers three-dimensional thermal diffusivity fields exactly by embedding a differentiable heat solver inside neural field optimization.

arxiv:2603.11045 v2 · 2026-03-11 · cs.LG · cond-mat.mtrl-sci · cs.AI · cs.CV · physics.ins-det

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

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

Across synthetic 3D benchmarks, NeFTY substantially outperforms soft-constrained PINN variants and a voxel-grid baseline on label-free volumetric recovery, and it transfers to real thermography data, surpassing classical signal-processing baselines in both defect segmentation and depth estimation.

C2weakest assumption

The assumption that a coordinate-based neural network can faithfully represent the unknown diffusivity field while the implicit-Euler discretization with harmonic-mean fluxes exactly captures the continuous PDE on the chosen grid for the materials and time scales of interest.

C3one line summary

NeFTY embeds a differentiable implicit-Euler heat solver into neural field optimization to solve the inverse heat conduction problem exactly on the discretization, outperforming soft PINNs and classical baselines on synthetic 3D benchmarks and real thermography data.

Formal links

1 machine-checked theorem link

Cited by

1 paper in Pith

Neural Fields for NV-Center Inverse Sensing

Receipt and verification

First computed	2026-05-17T23:39:15.807956Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

59ebc38ef9796aceffc304ddc1eea2a6b8e6d9e0d82969106f936afabb1766aa

Aliases

arxiv: 2603.11045 · arxiv_version: 2603.11045v2 · doi: 10.48550/arxiv.2603.11045 · pith_short_12: LHV4HDXZPFVM · pith_short_16: LHV4HDXZPFVM576D · pith_short_8: LHV4HDXZ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LHV4HDXZPFVM576DATO4D3VCU2 \
  | 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: 59ebc38ef9796aceffc304ddc1eea2a6b8e6d9e0d82969106f936afabb1766aa

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "83c2e1738631f768288fbea53d1192eeac1ba37b8a2611377493bb2b60a9d926",
    "cross_cats_sorted": [
      "cond-mat.mtrl-sci",
      "cs.AI",
      "cs.CV",
      "physics.ins-det"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-03-11T17:59:42Z",
    "title_canon_sha256": "6afb4f1099c43b711fd32d024947eb5205fd26a95e58cea2febfe014dc5dc3f7"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.11045",
    "kind": "arxiv",
    "version": 2
  }
}