An adaptive reduced basis ANOVA method for high-dimensional Bayesian inverse problems
For practitioners solving Bayesian inverse problems with expensive models and high-dimensional parameters, this method offers a way to reduce computational burden.
The paper introduces an adaptive reduced basis ANOVA method to accelerate Bayesian inverse problems with high-dimensional parameters, achieving significant computational cost reduction while maintaining accuracy.
In Bayesian inverse problems sampling the posterior distribution is often a challenging task when the underlying models are computationally intensive. To this end, surrogates or reduced models are often used to accelerate the computation. However, in many practical problems, the parameter of interest can be of high dimensionality, which renders standard model reduction techniques infeasible. In this paper, we present an approach that employs the ANOVA decomposition method to reduce the model with respect to the unknown parameters, and the reduced basis method to reduce the model with respect to the physical parameters. Moreover, we provide an adaptive scheme within the MCMC iterations, to perform the ANOVA decomposition with respect to the posterior distribution. With numerical examples, we demonstrate that the proposed model reduction method can significantly reduce the computational cost of Bayesian inverse problems, without sacrificing much accuracy.