pith:ZVO3SCT4
Hybrid Iterative Neural Low-Regularity Integrator for Nonlinear Dispersive Equations
Augmenting low-regularity integrators with scaled neural corrections yields global error C(ε_net + δ) τ^γ ln(1/τ) for nonlinear dispersive equations.
arxiv:2605.04853 v2 · 2026-05-06 · cs.LG
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Claims
Under stated assumptions, the global error satisfies C(ε_net + δ) τ^γ ln(1/τ); experiments on three dispersive benchmarks with rough data show improved accuracy over analytical integrators, splitting methods, and neural PDE surrogates, with stable spatial refinement and out-of-distribution transfer.
The neural correction's Lipschitz contribution remains O(τ) after explicit time-step scaling, and the network approximation quality ε_net plus training shortfall δ are small enough for the Gronwall factor to stay bounded independently of spatial resolution.
A hybrid solver-neural framework achieves global error O(τ^γ ln(1/τ)) for nonlinear dispersive equations by training a lightweight network on the residual defect inside the solver loop while preserving uniform stability.
Receipt and verification
| First computed | 2026-06-11T01:09:37.065467Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
cd5db90a7c799887aadbb766e98e0c1696f045764028b336e4c714c60effc62b
Aliases
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZVO3SCT4PGMIPKW3W5TOTDQMC2 \
| 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())"
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Canonical record JSON
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