AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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These results show that one-shot generative reconstruction can be effective for simpler settings, while hierarchical forecast-analysis refinement becomes advantageous in strongly multiscale and underdetermined regimes.
Two-Scale Localized PCA-Net is introduced, which decomposes the solution into a coarse-global component and local residual corrections and provides an efficient representation for artifact-reduced PDE operator learning.
Operator-learning surrogates have been benchmarked largely on single-field, single-interface problems, leaving unclear whether architectural choices validated in those settings transfer to constrained, multiphase flows. We introduce a three-phase interfacial-flow benchmark to examine whether the trunk coordinate repres...
This work proposes Quantum SEDONet (Spectral-Embedded Deep Operator Network), which assigns each trunk coordinate a spectral basis according to its boundary condition: Fourier features for periodic coordinates and Chebyshev features for bounded, non-periodic coordinates.
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