IKNO replaces first-order kernel integrals in neural operators with infinite-order versions that have efficient closed-form approximations and reports SOTA accuracy on time-dependent and time-independent benchmarks.
Rigno: A graph-based framework for robust and accurate operator learning for pdes on arbitrary domains.arXiv preprint arXiv:2501.19205
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Neural and spectral operators can approximate shape-to-solution maps for families of elliptic and parabolic PDEs and BIEs with provable uniform error bounds derived from parametric holomorphy on a reference domain.
AeTHERON achieves mean extrapolation MAE of 0.168 while qualitatively capturing vortex topology on unseen timesteps of flapping flexible caudal fin FSI simulations.
citing papers explorer
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IKNO: Infinite-order Kernel Neural Operators
IKNO replaces first-order kernel integrals in neural operators with infinite-order versions that have efficient closed-form approximations and reports SOTA accuracy on time-dependent and time-independent benchmarks.
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Neural Shape Operator Surrogates -- Expression Rate Bounds
Neural and spectral operators can approximate shape-to-solution maps for families of elliptic and parabolic PDEs and BIEs with provable uniform error bounds derived from parametric holomorphy on a reference domain.
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AeTHERON: Autoregressive Topology-aware Heterogeneous Graph Operator Network for Fluid-Structure Interaction
AeTHERON achieves mean extrapolation MAE of 0.168 while qualitatively capturing vortex topology on unseen timesteps of flapping flexible caudal fin FSI simulations.