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pith:2026:LFUVNLA672S3LU4U36SSLHLYY5
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Hypernetwork-Conditioned WENO5 Conservative-Form CNNs for One-Dimensional Conservation Laws

Wei Guo, Xinghui Zhong, Yongsheng Chen

Hypernetwork conditions a CNN to predict WENO weights from coarse initial data and mesh info while keeping the conservative finite-volume update.

arxiv:2605.13106 v1 · 2026-05-13 · math.NA · cs.NA

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Claims

C1strongest claim

Hyper--CFCNN attains accuracy comparable to classical WENO5, achieves near machine-precision conservation in the known-flux setting on fine meshes, and generalizes to unseen spatial resolutions and initial conditions without retraining.

C2weakest assumption

The hypernetwork, given only coarse descriptors of the initial condition plus mesh spacing and layout, can produce target-network parameters that yield stable, high-order weights for problems outside the training distribution.

C3one line summary

A hypernetwork conditions a conservative-form CNN to predict WENO5 weights from mesh and initial-condition metadata, preserving conservation and generalizing across resolutions for 1D hyperbolic conservation laws.

References

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[1] doi:10.1137/140951758 , journal = · doi:10.1137/140951758
[2] Nick Higham , title =
[3] Kolda and Ali Pinar , eprint =
[4] and Zhang, Shanrong and Merritt, Matthew E
[5] 2003 , eid = 2003 · doi:10.1103/physreve.68.026121
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First computed 2026-05-18T03:08:58.164500Z
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596956ac1efea5b5d394dfa5259d78c77c242c22ae18773fe7fad92b3de0a6a2

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arxiv: 2605.13106 · arxiv_version: 2605.13106v1 · doi: 10.48550/arxiv.2605.13106 · pith_short_12: LFUVNLA672S3 · pith_short_16: LFUVNLA672S3LU4U · pith_short_8: LFUVNLA6
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