Pith Number
pith:L63OQPX4
pith:2024:L63OQPX4BSSNOSUA72MBO4T5H7
not attested
not anchored
not stored
refs pending
Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network
arxiv:2411.14192 v1 · 2024-11-21 · physics.flu-dyn · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{L63OQPX4BSSNOSUA72MBO4T5H7}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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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.
Receipt and verification
| First computed | 2026-07-05T09:38:42.998409Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5fb6e83efc0ca4d74a80fe9817727d3ffdcd93b921a685d0adb9abc4aebde53e
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/L63OQPX4BSSNOSUA72MBO4T5H7 \
| 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: 5fb6e83efc0ca4d74a80fe9817727d3ffdcd93b921a685d0adb9abc4aebde53e
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "3f6db207dc0ea7b5b276b19f683151b126157957c50c812b2b502b7b5e7a557f",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"primary_cat": "physics.flu-dyn",
"submitted_at": "2024-11-21T15:01:17Z",
"title_canon_sha256": "9dcb540313006a08d9b6f6c317a4d95d57fa0f312f1cb4b1f113a822db3821e9"
},
"schema_version": "1.0",
"source": {
"id": "2411.14192",
"kind": "arxiv",
"version": 1
}
}