Bayesian inference using intermediate distribution based on coarse multiscale model for time fractional diffusion equation
For practitioners of Bayesian inversion in time fractional diffusion equations, this method reduces computational cost while maintaining accuracy.
The paper accelerates Bayesian inference for unknown inputs in time fractional diffusion models by constructing an intermediate distribution from a coarse multiscale model, enabling a surrogate over the posterior's significant region. The method achieves accuracy comparable to direct surrogate modeling with lower polynomial chaos order.
In the paper, we present a strategy for accelerating posterior inference for unknown inputs in time fractional diffusion models. In many inference problems, the posterior may be concentrated in a small portion of the entire prior support. It will be much more efficient if we build and simulate a surrogate only over the significant region of the posterior. To this end, we construct a coarse model using Generalized Multiscale Finite Element Method (GMsFEM), and solve a least-squares problem for the coarse model with a regularizing Levenberg-Marquart algorithm. An intermediate distribution is built based on the approximate sampling distribution. For Bayesian inference, we use GMsFEM and least-squares stochastic collocation method to obtain a reduced coarse model based on the intermediate distribution. To increase the sampling speed of Markov chain Monte Carlo, the DREAM$_\text{ZS}$ algorithm is used to explore the surrogate posterior density, which is based on the surrogate likelihood and the intermediate distribution. The proposed method with lower gPC order gives the approximate posterior as accurate as the the surrogate model directly based on the original prior. A few numerical examples for time fractional diffusion equations are carried out to demonstrate the performance of the proposed method with applications of the Bayesian inversion.