Extension of graph-accelerated non-intrusive polynomial chaos to high-dimensional uncertainty quantification through the active subspace method

TL;DR

This paper extends the graph-accelerated non-intrusive polynomial chaos method to high-dimensional uncertainty quantification by integrating it with the active subspace technique, significantly improving efficiency and accuracy in complex problems.

Contribution

It introduces AS-AMTC and AS-NIPC, novel methods combining active subspace dimension reduction with graph-accelerated polynomial chaos for high-dimensional UQ.

Findings

AS-NIPC reduces relative error by 30% in 81-dimensional problems.

AS-AMTC achieves an 80% reduction in error, demonstrating high efficiency.

Methods are validated on a complex air-taxi trajectory optimization scenario.

Abstract

The recently introduced graph-accelerated non-intrusive polynomial chaos (NIPC) method has shown effectiveness in solving a broad range of uncertainty quantification (UQ) problems with multidisciplinary systems. It uses integration-based NIPC to solve the UQ problem and generates the quadrature rule in a desired tensor structure, so that the model evaluations can be efficiently accelerated through the computational graph transformation method, Accelerated Model evaluations on Tensor grids using Computational graph transformations (AMTC). This method is efficient when the model's computational graph possesses a certain type of sparsity which is commonly the case in multidisciplinary problems. However, it faces limitations in high-dimensional cases due to the curse of dimensionality. To broaden its applicability in high-dimensional UQ problems, we propose AS-AMTC, which integrates the AMTC approach with the active subspace (AS) method, a widely-used dimension reduction technique. In developing this new method, we have also developed AS-NIPC, linking integration-based NIPC with the AS method for solving high-dimensional UQ problems. AS-AMTC incorporates rigorous approaches to generate orthogonal polynomial basis functions for lower-dimensional active variables and efficient quadrature rules to estimate their coefficients. The AS-AMTC method extends AS-NIPC by generating a quadrature rule with a desired tensor structure. This allows the AMTC method to exploit the computational graph sparsity, leading to efficient model evaluations. In an 81-dimensional UQ problem derived from an air-taxi trajectory optimization scenario, AS-NIPC demonstrates a 30% decrease in relative error compared to the existing methods, while AS-AMTC achieves an 80% reduction.