Related papers: Using Principal Component Analysis to Distinguish …
Principal component analysis (PCA) represents a standard approach to identify collective variables $\{x_i\}\!=\!\boldsymbol{x}$, which can be used to construct the free energy landscape $\Delta G(\boldsymbol{x})$ of a molecular system.…
The dynamics of quantized magnetic vortices and their pinning by materials defects determine electromagnetic properties of superconductors, particularly their ability to carry non-dissipative currents. Despite recent advances in the…
We investigate the pinning and driven dynamics of vortices interacting with twin boundaries using large scale molecular dynamics simulations on samples with near one million pinning sites. For low applied driving forces, the vortex lattice…
We use Principal Component Analysis (PCA) to study the gas dynamics in numerical simulations of typical MCs. Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas…
We study the dynamics of vortices in strongly disordered $d=2$ Josephson junction arrays and superconducting films driven by a current. We find a dynamic phase transition in vortex flow at a current $I_p>I_c$. Below $I_p$ there is plastic…
In the present paper which is a sequel to [N.B. Volkov and A.M. Iskoldsky The dynamics of vortex structures and states of current: 1;[1]], the dynamics of non-equilibrium phase transitions and states of current in electrophysical systems…
We study theoretically dynamical phases of vortices in superconducting films with arrays of obstacles. By performing a series of molecular dynamics simulations and analytical calculations, we demonstrate the existence of a phase of…
Principal component analysis (PCA) is arguably the most widely used approach for large-dimensional factor analysis. While it is effective when the factors are sufficiently strong, it can be inconsistent when the factors are weak and/or the…
We present molecular dynamics simulations of driven vortices in layered superconductors in the presence of an external homogeneous force and point disorder. We use a model introduced by J.R.Clem for describing 3D vortex lines as stacks of…
Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme…
Many agent based simulation approaches have been proposed for pedestrian flow. As such models are applied e.g.\ in evacuation studies, the quality and reliability of such models is of vital interest. Pedestrian trajectories are functional…
Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of…
We present measurements showing how disorder determines the dynamics of a vortex lattice. Using superconductor-normal-superconductor (SNS) arrays placed in finite magnetic fields, disorder is introduced by shifting the field away from…
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data.…
We study a 3D model of driven vortices in weakly coupled layered superconductors with strong pinning. Above the critical force $F_c$, we find a plastic flow regime in which pancakes in different layers are uncoupled, corresponding to a…
We study the dynamics of Josephson junction arrays with positional disorder and driven by an external current. We consider weak magnetic fields, corresponding to a frustration $f=n+1/25$ with $n$ integer. We find that above the critical…
The new dynamic phase diagram for driven vortices with varying lattice softness we present here indicates that, at high driving currents, at least two distinct dynamic phases of flux flow appear depending on the vortex-vortex interaction…
Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal processing, mechanical engineering, psychometrics, and other fields under different…
Principal component analysis (PCA) is a tool to capture factors that explain variation in data. Across domains, data are now collected across multiple contexts (for example, individuals with different diseases, cells of different types, or…
The mixed state of type II superconductors has magnetic flux penetrating the sample in the form of vortices, with each vortex carrying an identical quantum of flux. These vortices generally form a triangular lattice under weak mutually…