The flux flow properties of epitaxial niobium films with different pinning strengths are investigated by dc electrical resistance measurements and mapped to results derived within the framework of a theoretical model. Investigated are the cases of weak random pinning in as-grown films, strong random pinning in Ga ion-irradiated films, and strong periodic pinning induced by a nanogroove array milled by focused ion beam. The generic feature of the current-voltage curves of the films consists in instability jumps to the normal state at some instability current density j * as the vortex lattice reaches its critical velocity v * . While v * (B) monotonically decreases for as-grown films, the irradiated films exhibit a non-monotonic dependence v * (B) attaining a maximum in the low-field range. In the case of nanopatterned films, this broad maximum is accompanied by a much sharper maximum in both, v * (B) and j * (B), which we attribute to the commensurability effect when the spacing between the vortex rows coincides with the location of the grooves. We argue that the observed behavior of v * (B) can be explained by the pinning effect on the vortex flow instability and support our claims by fitting the experimental data to theoretical expressions derived within a model accounting for the field dependence of the depinning current density.
Switching dynamical systems are an expressive model class for the analysis of time-series data. As in many fields within the natural and engineering sciences, the systems under study typically evolve continuously in time, it is natural to consider continuous-time model formulations consisting of switching stochastic differential equations governed by an underlying Markov jump process. Inference in these types of models is however notoriously difficult, and tractable computational schemes are rare. In this work, we propose a novel inference algorithm utilizing a Markov Chain Monte Carlo approach. The presented Gibbs sampler allows to efficiently obtain samples from the exact continuous-time posterior processes. Our framework naturally enables Bayesian parameter estimation, and we also include an estimate for the diffusion covariance, which is oftentimes assumed fixed in stochastic differential equations models. We evaluate our framework under the modeling assumption and compare it against an existing variational inference approach.
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