Pith Number

pith:ERTGNUO3

pith:2026:ERTGNUO3TRC4WN4YJ5O3QOQQJO

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Generative modeling of granular flow on inclined planes using conditional flow matching

Rui Li, Teng Man, Xuyang Li, Yimin Lu

A conditional flow matching model reconstructs interior granular velocities and stresses from sparse boundary data by training on particle simulations and enforcing consistency via a differentiable forward operator.

arxiv:2604.04453 v2 · 2026-04-06 · cs.CE · cs.LG

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Claims

C1strongest claim

This study... presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations... The framework accurately recovers interior flow fields from full observation to only 16% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime.

C2weakest assumption

That a model trained exclusively on high-fidelity particle-resolved discrete element simulations, when guided at inference by a differentiable forward operator, will produce physically consistent reconstructions from real-world sparse boundary observations without introducing unphysical artifacts or requiring hyperparameter tuning.

C3one line summary

A conditional flow matching model trained on DEM simulations reconstructs granular flow velocity fields from as little as 11-16% sparse boundary data, outperforming deterministic CNN baselines while providing uncertainty estimates via ensemble generation.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-26T01:03:29.443275Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

246666d1db9c45cb37984f5db83a104b880ffc072934dbb0ea894158cb4961de

Aliases

arxiv: 2604.04453 · arxiv_version: 2604.04453v2 · doi: 10.48550/arxiv.2604.04453 · pith_short_12: ERTGNUO3TRC4 · pith_short_16: ERTGNUO3TRC4WN4Y · pith_short_8: ERTGNUO3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ERTGNUO3TRC4WN4YJ5O3QOQQJO \
  | 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: 246666d1db9c45cb37984f5db83a104b880ffc072934dbb0ea894158cb4961de

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "290d8fdcf6cebf98b6a416a0fc32e5c320c1a002a20abf5ad2c857cfb1bd80a2",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CE",
    "submitted_at": "2026-04-06T05:59:54Z",
    "title_canon_sha256": "deb0dbfa33875a239e2d499b84168c70c4873beebd01fe6c2667b735116f3f5c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.04453",
    "kind": "arxiv",
    "version": 2
  }
}