Rank adaptive tensor recovery based model reduction for partial differential equations with high-dimensional random inputs
For researchers solving PDEs with high-dimensional random parameters, this method reduces computational cost while maintaining accuracy.
This work proposes a model reduction approach using rank adaptive tensor recovery for PDEs with high-dimensional random inputs, achieving efficient stochastic collocation approximations. Numerical experiments demonstrate its efficiency.
This work proposes a systematic model reduction approach based on rank adaptive tensor recovery for partial differential equation (PDE) models with high-dimensional random parameters. Since the standard outputs of interest of these models are discrete solutions on given physical grids which are high-dimensional, we use kernel principal component analysis to construct stochastic collocation approximations in reduced dimensional spaces of the outputs. To address the issue of high-dimensional random inputs, we develop a new efficient rank adaptive tensor recovery approach to compute the collocation coefficients. Novel efficient initialization strategies for non-convex optimization problems involved in tensor recovery are also developed in this work. We present a general mathematical framework of our overall model reduction approach, analyze its stability, and demonstrate its efficiency with numerical experiments.