Pith Number
pith:HHRZ2HGO
pith:2020:HHRZ2HGOSA5ORU7EZKR4BGISMP
not attested
not anchored
not stored
refs pending
On the use of recurrent neural networks for predictions of turbulent flows
arxiv:2002.01222 v1 · 2020-02-04 · physics.flu-dyn · cs.LG · physics.comp-ph
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{HHRZ2HGOSA5ORU7EZKR4BGISMP}
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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 | 2026-07-05T01:00:16.137012Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
39e39d1cce903ae8d3e4caa3c0991263c119d1333775b3c2a17911f925545e0a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/HHRZ2HGOSA5ORU7EZKR4BGISMP \
| 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: 39e39d1cce903ae8d3e4caa3c0991263c119d1333775b3c2a17911f925545e0a
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "72b0613e11fdad5ab78e6cab2adbde1700f0accb4e8ae7ff9c5e2c3744603761",
"cross_cats_sorted": [
"cs.LG",
"physics.comp-ph"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "physics.flu-dyn",
"submitted_at": "2020-02-04T11:01:43Z",
"title_canon_sha256": "8518ec172150acd45f2c7cdcb7312712b24032d527635f286d6f0ddc659ae4ff"
},
"schema_version": "1.0",
"source": {
"id": "2002.01222",
"kind": "arxiv",
"version": 1
}
}