A transport map learned by normalizing flows turns Smolyak sparse Gauss-Hermite quadrature nodes into nodes for a non-Gaussian distribution, enabling expectation estimates for PDE outputs with far fewer simulation runs.
Integrability and Approximability of Solutions to the Stationary Diffusion Equation with L\'evy Coefficient
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abstract
We investigate the stationary diffusion equation with a coefficient given by a (transformed) L\'evy random field. L\'evy random fields are constructed by smoothing L\'evy noise fields with kernels from the Mat\'ern class. We show that L\'evy noise naturally extends Gaussian white noise within Minlos' theory of generalized random fields. Results on the distributional path spaces of L\'evy noise are derived as well as the amount of smoothing to ensure such distributions become continuous paths. Given this, we derive results on the pathwise existence and measurability of solutions to the random boundary value problem (BVP). For the solutions of the BVP we prove existence of moments (in the $H^1$-norm) under adequate growth conditions on the L\'evy measure of the noise field. Finally, a kernel expansion of the smoothed L\'evy noise fields is introduced and convergence in $L^n$ ($n\geq 1$) of the solutions associated with the approximate random coefficients is proven with an explicit rate.
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Learning to Integrate
A transport map learned by normalizing flows turns Smolyak sparse Gauss-Hermite quadrature nodes into nodes for a non-Gaussian distribution, enabling expectation estimates for PDE outputs with far fewer simulation runs.