Smoothness and other hyperparameter estimation for inverse problems related to data assimilation

TL;DR

This paper develops a hierarchical Bayesian approach for estimating hyperparameters, especially smoothness, in inverse problems related to data assimilation, improving uncertainty quantification and parameter estimation accuracy.

Contribution

It introduces a hyperparameter estimation method within a hierarchical Bayesian framework for inverse problems in data assimilation, using efficient Metropolis-within-Gibbs sampling.

Findings

Joint hyperparameter estimation reduces errors in uncertainty quantification.

Method performs well on Navier-Stokes and advection-diffusion equations.

Estimates achieve accuracy comparable to known smoothness scenarios.

Abstract

We consider Bayesian inverse problems arising in data assimilation for dynamical systems governed by partial and stochastic partial differential equations. The space-time dependent field is inferred jointly with static parameters of the prior and likelihood densities. Particular emphasis is placed on the hyperparameter controlling the prior smoothness and regularity, which is critical in ensuring well-posedness, shaping posterior structure, and determining predictive uncertainty. Commonly it is assumed to be known and fixed a priori; however in this paper we will adopt a hierarchical Bayesian framework in which smoothness and other hyperparameters are treated as unknown and assigned hyperpriors. Posterior inference is performed using Metropolis-within-Gibbs sampling suitable to high dimensions, for which hyperparameter estimation involves little computational overhead. The methodology is demonstrated on inverse problems for the Navier-Stokes equations and the stochastic advection-diffusion equation, under sparse and dense observation regimes, using Gaussian priors with different covariance structure. Numerical results show that jointly estimating the smoothness substantially reduces the errors in uncertainty quantification and parameter estimation induced by smoothness misspecification, by achieving performance comparable to scenarios in which the true smoothness is known.