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Found 2 papers, 1 papers with code
Kernel Methods are Competitive for Operator Learning
We present a general kernel-based framework for learning operators between Banach spaces along with a priori error analysis and comprehensive numerical comparisons with popular neural net (NN) approaches such as Deep Operator Net (DeepONet) [Lu et al.] and Fourier Neural Operator (FNO) [Li et al.].
One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels
(2) Complete the graph (approximate unknown functions and random variables) via Maximum a Posteriori Estimation (given the data) with Gaussian Process (GP) priors on the unknown functions.
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