Pith Number
pith:H7ORN5GU
pith:2024:H7ORN5GU6U5LQB7E2ICJGXP5KK
not attested
not anchored
not stored
refs pending
A machine learning approach to predict near-optimal meshes for turbulent compressible flow simulations
arxiv:2406.16057 v1 · 2024-06-23 · cs.CE
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{H7ORN5GU6U5LQB7E2ICJGXP5KK}
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
· sign in to
claim
4
Citations
5
Replications
✓
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 | 2026-07-05T08:35:52.530596Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
3fdd16f4d4f53ab807e4d204935dfd5296c7ff203a3f835eec3db43e19fa27ad
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/H7ORN5GU6U5LQB7E2ICJGXP5KK \
| 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: 3fdd16f4d4f53ab807e4d204935dfd5296c7ff203a3f835eec3db43e19fa27ad
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "4077ffe0eaa59446f0edb6c45fd264c6d2e514b8142364fcf376738ab338e0b0",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CE",
"submitted_at": "2024-06-23T09:37:00Z",
"title_canon_sha256": "b65eabedfb713a9ebffb56df653891ce8506e8d10f905084beac90940111c8f0"
},
"schema_version": "1.0",
"source": {
"id": "2406.16057",
"kind": "arxiv",
"version": 1
}
}