Pith Number
pith:GD2MMKCQ
pith:2024:GD2MMKCQWBYAVDRESV4Z5DGRVR
not attested
not anchored
not stored
refs pending
Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach
arxiv:2411.13366 v2 · 2024-11-20 · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{GD2MMKCQWBYAVDRESV4Z5DGRVR}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
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Bitcoin timestamp
2
Internet Archive
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4
Citations
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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 | 2026-07-05T09:38:33.354272Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
30f4c62850b0700a8e2495799e8cd1ac5e888b8e1f5e432a10b672b71dd80300
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GD2MMKCQWBYAVDRESV4Z5DGRVR \
| 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: 30f4c62850b0700a8e2495799e8cd1ac5e888b8e1f5e432a10b672b71dd80300
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "f77caabea3e80c98ecdb739f8dd9717557f76f473cfdde0c91ffb2c9e23c8589",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2024-11-20T14:42:53Z",
"title_canon_sha256": "734cf80654680a94d3a62ebb1cfec5f94e729731c3b0358e9afff94d1d9313bc"
},
"schema_version": "1.0",
"source": {
"id": "2411.13366",
"kind": "arxiv",
"version": 2
}
}