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Found 3 papers, 1 papers with code
Sampling via Gradient Flows in the Space of Probability Measures
Our third contribution is to study, and develop efficient algorithms based on Gaussian approximations of the gradient flows; this leads to an alternative to particle methods.
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
We introduce a priori Sobolev-space error estimates for the solution of nonlinear, and possibly parametric, PDEs using Gaussian process and kernel based methods.
Solving and Learning Nonlinear PDEs with Gaussian Processes
The main idea of our method is to approximate the solution of a given PDE as the maximum a posteriori (MAP) estimator of a Gaussian process conditioned on solving the PDE at a finite number of collocation points.
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