Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems

TL;DR

This paper introduces BINO, a neural operator-based Bayesian method for efficiently solving forward and inverse subdiffusion problems modeled by fractional diffusion equations, reducing computational costs and storage requirements.

Contribution

It presents a novel neural operator framework combined with Bayesian inversion to efficiently address forward and inverse subdiffusion problems.

Findings

Efficiently solves forward subdiffusion problems with high accuracy.

Reduces computational time and storage compared to traditional methods.

Successfully models inverse problems in subdiffusion processes.

Abstract

Fractional diffusion equations have been an effective tool for modeling anomalous diffusion in complicated systems. However, traditional numerical methods require expensive computation cost and storage resources because of the memory effect brought by the convolution integral of time fractional derivative. We propose a Bayesian Inversion with Neural Operator (BINO) to overcome the difficulty in traditional methods as follows. We employ a deep operator network to learn the solution operators for the fractional diffusion equations, allowing us to swiftly and precisely solve a forward problem for given inputs (including fractional order, diffusion coefficient, source terms, etc.). In addition, we integrate the deep operator network with a Bayesian inversion method for modelling a problem by subdiffusion process and solving inverse subdiffusion problems, which reduces the time costs (without suffering from overwhelm storage resources) significantly. A large number of numerical experiments demonstrate that the operator learning method proposed in this work can efficiently solve the forward problems and Bayesian inverse problems of the subdiffusion equation.