A renormalization approach for the 2D Anderson model at the band edge: Scaling of the localization volume

We study the localization volumes $V$ (participation ratio) of electronic wave functions in the 2d-Anderson model with diagonal disorder. Using a renormalization procedure, we show that at the band edges, i.e. for energies $E\approx \pm 4$, $V$ is inversely proportional to the variance \var of the site potentials. Using scaling arguments, we show that in the neighborhood of $E=\pm 4$, $V$ scales as V=\var^{-1}g((4-\ve E\ve)/\var) with the scaling function $g(x)$. Numerical simulations confirm this scaling ansatz

Scaling of the localization length in linear electronic and vibrational systems with long-range correlated disorder

The localization lengths of long-range correlated disordered chains are studied for electronic wavefunctions in the Anderson model and for vibrational states. A scaling theory close to the band edge is developed in the Anderson model and supported by numerical simulations. This scaling theory is mapped onto the vibrational case at small frequencies. It is shown that for small frequencies, unexpectateley the localization length is smaller for correlated than for uncorrelated chains.Comment: to be published in PRB, 4 pages, 2 Figure

Localized low-frequency Neumann modes in 2d-systems with rough boundaries

We compute the relative localization volumes of the vibrational eigenmodes in two-dimensional systems with a regular body but irregular boundaries under Dirichlet and under Neumann boundary conditions. We find that localized states are rare under Dirichlet boundary conditions but very common in the Neumann case. In order to explain this difference, we utilize the fact that under Neumann conditions the integral of the amplitudes, carried out over the whole system area is zero. We discuss, how this condition leads to many localized states in the low-frequency regime and show by numerical simulations, how the number of the localized states and their localization volumes vary with the boundary roughness.Comment: 7 pages, 4 figure

Scaling behavior of a one-dimensional correlated disordered electronic System

A one-dimensional diagonal tight binding electronic system with correlated disorder is investigated. The correlation of the random potential is exponentially decaying with distance and its correlation length diverges as the concentration of "wrong sign" approaches to 1 or 0. The correlated random number sequence can be generated easily with a binary sequence similar to that of a one-dimensional spin glass system. The localization length (LL) and the integrated density of states (IDOS) for long chains are computed. A comparison with numerical results is made with the recently developed scaling technique results. The Coherent Potential Approximation (CPA) is also adopted to obtain scaling functions for both the LL and the IDOS. We confirmed that the scaling functions show a crossover near the band edge and establish their relation to the concentration. For concentrations near to 0 or 1 (longer correlation length case), the scaling behavior is followed only for a very limited range of the potential strengths.Comment: will appear in PR

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within. This enables the model to quickly adapt to new environments of unknown dynamics within a few observations. We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world. Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video: https://www.youtube.com/watch?v=FrPpP7aW3L