CLNO: Conservative Local Neural Operator for Compressible Flow Prediction
We propose a conservative local neural operator (CLNO) for accurate and generalizable autoregressive prediction of compressible flows. Building upon the locality and translation invariance of the local neural operator (LNO), CLNO introduces a conservative evolution framework based on local subdomains of fixed physical size, separating local mean-state evolution from fine-scale reconstruction. The flow is encoded into subdomain-integrated conservative states and fine-scale features, while a shared local network predicts interface fluxes for finite-volume updates that enforce discrete conservation. A convolutional reconstruction network recovers fine-scale structures using zero-mean basis f...
| Authors | Buxuan Wang, Rongzu Li, Qingsong Zou |
|---|---|
| Primary category | math.NA |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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