DINO decomposes turbulent evolution into parallel local differential and global integral operators to achieve stable autoregressive forecasting on 2D Kolmogorov flow.
Hamiltonian neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 2representative citing papers
Presents hue-, saturation-, luminance-equivariant GCNNs via a direct-image lifting layer that resolves invalid RGB issues in prior CEConv work and reports better OOD generalization plus sample efficiency.
citing papers explorer
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Differential-Integral Neural Operator for Long-Term Turbulence Forecasting
DINO decomposes turbulent evolution into parallel local differential and global integral operators to achieve stable autoregressive forecasting on 2D Kolmogorov flow.
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Learning Color Equivariant Representations
Presents hue-, saturation-, luminance-equivariant GCNNs via a direct-image lifting layer that resolves invalid RGB issues in prior CEConv work and reports better OOD generalization plus sample efficiency.