Related papers: Using Principal Component Analysis to Distinguish …
Using numerical simulations of magnetically interacting vortices in disordered layered superconductors we obtain the static vortex phase diagram as a function of magnetic field and temperature. For increasing field or temperature, we find a…
We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to…
Principal Component analysis (PCA) is a useful statistical technique that is commonly used for multivariate analysis of correlated variables. It is usually applied as a dimension reduction method: the top principal components (PCs)…
Complexity is often exhibited in dynamical systems, where certain parameters evolve with time in a strange and chaotic nature. These systems lack predictability and are common in the physical world. Dissipative systems are one of such…
The vortex lattice is an ideal system to study the competition and interplay between interaction and disorder. New results on 2H-NbSe_2 (in samples of progressively increasing pinning) elucidate how pinning alters the phase boundary…
Using large-scale simulations on parallel processors, we analyze in detail the dynamical behavior of superconducting vortices undergoing avalanches. In particular, we quantify the effect of the pinning landscape on the macroscopic…
We study numerically the motion of vortices in dirty type II superconductors. In two dimensions at strong driving currents, vortices form highly correlated ``static channels''. The static structure factor exhibits convincing scaling…
Vortex matter in layered high-$T_c$ superconductors, including iron-pnictides, undergo several thermodynamic phase transitions due to the complex interplay of pinning energy, thermal energy and elastic energy. Moreover, the presence of…
The dynamics of vortices in a type-II superconductor with defects are studied by solving the time-dependent Ginzburg-Landau equations in two and three dimensions. We show that vortex flux tubes are trapped by volume defects up to a critical…
Within the class of stochastic cellular automata models of traffic flows, we look at the velocity dependent randomization variant (VDR-TCA) whose parameters take on a specific set of extreme values. These initial conditions lead us to the…
We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information…
We analyze the large-scale dynamics of vortex lattices and charge density waves driven in a disordered potential. Using a perturbative coarse-graining procedure we present an explicit derivation of non-equilibrium terms in the renormalized…
The work presented in my doctoral thesis is an experimental investigation of the nature of order to disorder transition of vortex lattice in a Type-II superconductor namely Co-intercalated 2H-NbSe2 using scanning tunnelling spectroscopy and…
Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and thus, various robust PCA methods have been proposed.…
The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis…
We report a crucial experimental test of the present models of the peak effect in weakly disordered type-II superconductors. Our results favor the scenario in which the peak effect arises from a crossover between the Larkin pinning length…
A system with many degrees of freedom can be characterized by a covariance matrix; principal components analysis (PCA) focuses on the eigenvalues of this matrix, hoping to find a lower dimensional description. But when the spectrum is…
The dynamics of vortices in type II superconductors exhibit a variety of patterns whose origin is poorly understood. This is partly due to the nonlinearity of the vortex mobility which gives rise to singular behavior in the vortex…
In this brief note, we formulate Principal Component Analysis (PCA) over datasets consisting not of points but of distributions, characterized by their location and covariance. Just like the usual PCA on points can be equivalently derived…
Vortices in electron fluids are a key indicator of electron hydrodynamics. However, a comprehensive framework linking macroscopic vorticity measurements with microscopic interactions and scattering mechanisms has been lacking. We employ…