Bayesian deep learning for system identification

Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent...

Hydraulic modelling of liquid-solid fluidisation in drinking water treatment processes

In drinking water treatment plants, multiphase flows are a frequent phenomenon. Examples of such flows are pellet-softening and filter backwashing where liquid-solid fluidisation is applied. A better grasp of these fluidisation processes is needed to be able to determine optimal hydraulic states. In this research, models were developed, and...

Information Propagation in Complex Networks: Structures and Dynamics

This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model which allows to focus on a very fundamental...

Particle enhanced foam flow in porous media near the critical micelle concentration

This thesis was performed in the framework of ErasmusMundus EU-INDIA scholarship programme. The main goal is to elucidate particle enhanced foam flow (surfactant water and nitrogen gas) in porous media near the critical micelle concentration. The thesis is divided in four parts: in the first part the modeling of foam flow is investigated, in the...