Development of hp-inverse model by using generalized polynomial chaos

TL;DR

This paper introduces an hp-inverse model using generalized polynomial chaos to estimate source functions from limited data, offering hierarchical basis functions and sparse solutions for improved accuracy in inverse problems.

Contribution

The paper develops an hp-inverse model combining gPC and GRBF for hierarchical basis functions, enabling effective source estimation with limited data and sparse regularization.

Findings

The hp-inverse model outperforms standard methods with limited data.

It achieves accurate source estimation even when unknowns vastly outnumber observations.

The model demonstrates robustness with a high m/n ratio, e.g., greater than 40.

Abstract

We present a hp-inverse model to estimate a smooth, non-negative source function from a limited number of observations for a two-dimensional linear source inversion problem. A standard least-square inverse model is formulated by using a set of Gaussian radial basis functions (GRBF) on a rectangular mesh system with a uniform grid space. Here, the choice of the mesh system is modeled as a random variable and the generalized polynomial chaos (gPC) expansion is used to represent the random mesh system. It is shown that the convolution of gPC and GRBF provides hierarchical basis functions for the linear source inverse model with the $hp$-refinement capability. We propose a mixed l_1 and l_2 regularization to exploit the hierarchical nature of the basis functions to find a sparse solution. The $hp$-inverse model has an advantage over the standard least-square inverse model when the number of data is limited. It is shown that the hp-inverse model provides a good estimate of the source function even when the number of unknown parameters ($m$) is much larger the number of data ($n$), e.g., m/n > 40.