Hypernetworks map a forcing parameter directly to policy weights in an RL framework, enabling unified stabilization of the Kuramoto-Sivashinsky equation across regimes with KAN architectures showing strongest extrapolation.
Fourier neural operator for parametric partial differential equations
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GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.
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Hyperfastrl: Hypernetwork-based reinforcement learning for unified control of parametric chaotic PDEs
Hypernetworks map a forcing parameter directly to policy weights in an RL framework, enabling unified stabilization of the Kuramoto-Sivashinsky equation across regimes with KAN architectures showing strongest extrapolation.
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Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.