Related papers: Using Principal Component Analysis to Distinguish …
We numerically investigate collective ordering and disordering effects for vortices in type-II superconductors interacting with square and triangular substrate arrays under a dc drive that is slowly rotated with respect to the fixed…
A field theoretical method is developed which permits us to study the dynamics of vortices in disordered environments. In particular, we obtain a self-consistent system of equations for disorder averaged quantities. Making use of a…
We extend the principal component analysis (PCA) to second-order stationary vector time series in the sense that we seek for a contemporaneous linear transformation for a $p$-variate time series such that the transformed series is segmented…
We present numerical simulation results of driven vortex lattices in presence of random disorder at zero temperature. We show that the plastic dynamics is readily understood in the framework of chaos theory. Intermittency "routes to chaos"…
The dynamical phase diagrams for vortices in clean films, driven by an uniform force, and interacting with periodic pinning resulting from a columnar-defect lattice are investigated by numerical simulations of a London model, and other…
We study superconductors with two order components and phase separation driven by intercomponent density-density interaction, focusing on the phase where only one condensate has non-zero ground-state density and a competing order parameter…
We review our studies of elastic lattices driven by an external force $F$ in the presence of random disorder, which correspond to the case of vortices in superconducting thin films driven by external currents. Above a critical force $F_c$…
Depinning and nonequilibrium transitions within sliding states in systems driven over quenched disorder arise across a wide spectrum of size scales ranging from atomic friction at the nanoscale, flux motion in type-II superconductors at the…
Principal Component Analysis (PCA) is a well-known multivariate technique used to decorrelate a set of vectors. PCA has been extensively applied in the past to the classification of stellar and galaxy spectra. Here we apply PCA to the…
We investigated vortex dynamics in a single-crystal sample of type-II superconductor NbSe$_{2}$ using scanning tunneling microscopy at 4.2 K. The decay of the magnetic field at a few nT/s in our superconducting magnet induced the…
Modal analysis techniques are used to identify patterns and develop reduced-order models in a variety of fluid applications. However, experimentally acquired flow fields may be corrupted with incorrect and missing entries, which may degrade…
We use a coarse-grained model of superconducting vortices driven through a random pinning potential to study the nonlinear current-voltage (IV) characteristics of flux flow in type II superconductors with pinning. In experiments, the IV…
Principal component analysis (PCA) is largely adopted for chemical process monitoring and numerous PCA-based systems have been developed to solve various fault detection and diagnosis problems. Since PCA-based methods assume that the…
In the mixed state of type II superconductors, vortices penetrate the sample and form a correlated system due to the screening of supercurrents around them. Interestingly, we can study this correlated system as a function of density and…
Principal Component Analysis (PCA) via Singular Value Decomposition (SVD) of large datasets is an adaptive exploratory method to uncover natural patterns underlying the data. Several recent applications of the PCA-SVD to event-by-event…
We use large-scale parallel simulations to compute the motion of superconducting magnetic vortices during avalanches triggered by small field increases. We find that experimentally observable voltage bursts correspond to pulsing vortex…
Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order moments and is therefore fragile in the presence of heavy-tailed data and impulsive noise.…
We present the results of molecular dynamic simulations of a two-dimensional vortex array driven by a uniform current through random pinning centers at zero temperature. We identify two types of flow of the driven array near the depinning…
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA…
Characterizing complex many-body phases of matter has been a central question in quantum physics for decades. Numerical methods built around approximations of the renormalization group (RG) flow equations have offered reliable and…