The LC-prior GP combines POD-reduced coefficients with a physics-corrected prior and RBF-FD data generation to surrogate nonlinear multi-coupled PDEs on irregular 2D domains more efficiently than standard approaches.
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Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
The LC-prior GP combines POD-reduced coefficients with a physics-corrected prior and RBF-FD data generation to surrogate nonlinear multi-coupled PDEs on irregular 2D domains more efficiently than standard approaches.