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Einstein's stochastic description of the random movement of small objects in a fluid, i.e. Brownian motion, reveals to be quite different, when observed on short timescales. The limitations of Einstein's theory with respect to particle inertia and hydrodyn ...
We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional probability density process for the value of an asset at some specified time in the future. In the case where the asset is driven by Browni ...
The topic of this thesis is the study of several stochastic control problems motivated by sailing races. The goal is to minimize the travel time between two locations, by selecting the fastest route in face of randomly changing weather conditions, such as ...
At timescales once deemed immeasurably small by Einstein, the random movement of Brownian particles in a liquid is expected to be replaced by ballistic motion. So far, an experimental verification of this prediction has been out of reach due to a lack of i ...
A self-consistent modelling of ion cyclotron resonance heating (ICRH) is reviewed with the aim of obtaining a fast robust scheme suitable for routine simulation for transport codes and data analysis. Due to the complexity of calculating the wave field and ...
To quantify the randomness of Markov trajectories with fixed initial and final states, Ekroot and Cover proposed a closed-form expression for the entropy of trajectories of an irreducible finite state Markov chain. Numerous applications, including the stud ...
This paper presents a novel in-liquid method to manipulate and micro-assemble MEMS in 3D by means of holographic optical trapping and hydrophobic interaction. Up to eight traps can be simultaneously generated with a trapping stiffness of 5 pN/mu m each. SU ...
Transport of suprathermal ions is examined from the Levy walk perspective in a simple magnetized toroidal plasma. Depending on their energy, these suprathermal ions exhibit superdiffusive, diffusive, or subdiffusive dispersion as a result of the complex in ...
Many recent algorithms for sparse signal recovery can be interpreted as maximum-a-posteriori (MAP) estimators relying on some specific priors. From this Bayesian perspective, state-of-the-art methods based on discrete-gradient regularizers, such as total-v ...
Many recent algorithms for sparse signal recovery can be interpreted as maximum-a-posteriori (MAP) estimators relying on some specific priors. From this Bayesian perspective, state-of-the-art methods based on discrete-gradient regularizers, such as total- ...