pith:LMABHWB4
Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs
A stochastic zeroth-order estimator trains physics-informed neural networks with up to 10 million dimensions using memory and computation costs that stay independent of dimension and derivative order.
arxiv:2603.24002 v2 · 2026-03-25 · cs.LG
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\pithnumber{LMABHWB4WBMACZ44ENLJZU5DGJ}
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Record completeness
Claims
SDZE achieves dimension-independent complexity in both space and memory, enabling the training of 10-million-dimensional PINNs on a single NVIDIA A100 GPU.
That Common Random Numbers Synchronization algebraically cancels the O(1/ε²) variance explosion while preserving unbiasedness and convergence of the zeroth-order estimator for the specific randomized spatial operators used in high-order PINNs.
SDZE uses common random number synchronization and implicit subspace projection to enable training of 10-million-dimensional PINNs on a single GPU with O(1) space and memory complexity.
Formal links
Receipt and verification
| First computed | 2026-05-18T02:45:04.579245Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5b0013d83cb05801679c23569cd3a33279b5c8b90576da24f7d372de0760dcd0
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LMABHWB4WBMACZ44ENLJZU5DGJ \
| 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: 5b0013d83cb05801679c23569cd3a33279b5c8b90576da24f7d372de0760dcd0
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2026-03-25T07:02:34Z",
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