pith:DVEKH3HK
Hard-constrained Physics-informed Neural Networks for Interface Problems
Hard-constrained PINN formulations embed interface continuity and flux conditions directly into the neural network solution representation.
arxiv:2604.08453 v2 · 2026-04-09 · math.NA · cs.NA · physics.comp-ph
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Record completeness
Claims
We introduce two ansatz-based hard-constrained PINN formulations for interface problems that embed the interface physics into the solution representation and thereby decouple interface enforcement from PDE residual minimization.
That the windowing construction remains stable for general interface geometries and that the discrete buffer correction points can be chosen without introducing new fitting parameters that affect accuracy.
Hard-constrained PINN formulations via windowing and buffer approaches enforce interface conditions by design and outperform soft-constrained baselines on 1D and 2D elliptic interface problems.
Receipt and verification
| First computed | 2026-05-20T00:02:11.353662Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1d48a3eceafea6f1ee66682d2571f3507bbbca90f62b5b76cf49166b94a3d3dc
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DVEKH3HK72TPD3TGNAWSK4PTKB \
| 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: 1d48a3eceafea6f1ee66682d2571f3507bbbca90f62b5b76cf49166b94a3d3dc
Canonical record JSON
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