Graph Kernel Networks learn PDE solution operators that generalize across discretization methods and grid resolutions using graph-based kernel integration.
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A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
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Neural Operator: Graph Kernel Network for Partial Differential Equations
Graph Kernel Networks learn PDE solution operators that generalize across discretization methods and grid resolutions using graph-based kernel integration.
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.