An accurate estimation of ionospheric variables such as the total electron content (TEC) is
important for many space weather, communication, and satellite geodetic applications …

The Statistical Finite Element Method (statFEM) offers a Bayesian framework for integrating
computational models with observational data, thus providing improved predictions for …

Data assimilation algorithms integrate prior information from numerical model simulations
with observed data. Ensemble-based filters, regarded as state-of-the-art, are widely …

We address the computational efficiency of finding the A-optimal Bayesian experimental
design, where the observation map is based on partial differential equations and thus …

In this paper, we present a new ensemble-based filter method by reconstructing the analysis
step of the particle filter through a transport map, which directly transports prior particles to …

In this paper, we present the parameter identification for inelastic and multi-scale problems.
First, the theoretical background of several fundamental methods used in the upscaling …

Target tracking is an application widely used in various fields, such as surveillance, robotics,
and navigation, where the objective is to estimate the position and velocity of a moving …

In this thesis, a novel approach is developed for visco-plasticity and nonlinear dynamics
problems. In particular, variational equations are elaborated following the Helligner …

Pulmonary alveolus segmentation plays an important role in the diagnosis of alveolar
emphysema and lobar pneumonia. Besides, if the alveoli could be accurately segmented in …

We propose an affine-mapping based variational ensemble Kalman filter for sequential
Bayesian filtering problems with generic observation models. Specifically, the proposed …