pith:WW65ATP3
Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
The H^{-1} norm of any functional is equivalent to its expected squared evaluation against a random test function whose distribution depends only on the domain.
arxiv:2605.03542 v2 · 2026-05-05 · math.NA · cs.LG · cs.NA
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\pithnumber{WW65ATP3VJFPHKAC2CKKY5XM4M}
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Record completeness
Claims
We prove that the H^{-1} norm of any functional is equivalent to its expected squared evaluation against a random test function whose distribution depends only on the domain.
That there exists a random test function distribution depending only on the domain such that averaging squared evaluations exactly recovers the H^{-1} norm and the weak topology independently of the differential operator.
H^{-1} norm equivalence to expected squared evaluations on domain-dependent random test functions enables SV-PINNs that recover accurate solutions to challenging second-order elliptic PDEs faster than standard PINNs.
Receipt and verification
| First computed | 2026-06-26T01:15:19.025453Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b5bdd04dfbaa4af3a802d094ac76ece31accb5968cc32374ee04f2f9e66a6682
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/WW65ATP3VJFPHKAC2CKKY5XM4M \
| 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: b5bdd04dfbaa4af3a802d094ac76ece31accb5968cc32374ee04f2f9e66a6682
Canonical record JSON
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