Tensor Train Optimization for Polynomial Chaos for High Dimensional Uncertainty Quantification

Polynomial chaos methods are powerful tools for quantifying the impact of uncertainty in circuit parameters, however they suffer from the curse of dimensionality. This paper presents a novel approach to address this issue by combining the polynomial chaos expansion with the tensor train decomposition. The performance of our proposed approach is verified on a transmission line network circuit and compared to existing state of the art methods.