8,134 research outputs found
Anisotropic diffusion in continuum relaxation of stepped crystal surfaces
We study the continuum limit in 2+1 dimensions of nanoscale anisotropic
diffusion processes on crystal surfaces relaxing to become flat below
roughening. Our main result is a continuum law for the surface flux in terms of
a new continuum-scale tensor mobility. The starting point is the Burton,
Cabrera and Frank (BCF) theory, which offers a discrete scheme for atomic steps
whose motion drives surface evolution. Our derivation is based on the
separation of local space variables into fast and slow. The model includes: (i)
anisotropic diffusion of adsorbed atoms (adatoms) on terraces separating steps;
(ii) diffusion of atoms along step edges; and (iii) attachment-detachment of
atoms at step edges. We derive a parabolic fourth-order, fully nonlinear
partial differential equation (PDE) for the continuum surface height profile.
An ingredient of this PDE is the surface mobility for the adatom flux, which is
a nontrivial extension of the tensor mobility for isotropic terrace diffusion
derived previously by Margetis and Kohn. Approximate, separable solutions of
the PDE are discussed.Comment: 14 pages, 1 figur
Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition
Hierarchical uncertainty quantification can reduce the computational cost of
stochastic circuit simulation by employing spectral methods at different
levels. This paper presents an efficient framework to simulate hierarchically
some challenging stochastic circuits/systems that include high-dimensional
subsystems. Due to the high parameter dimensionality, it is challenging to both
extract surrogate models at the low level of the design hierarchy and to handle
them in the high-level simulation. In this paper, we develop an efficient
ANOVA-based stochastic circuit/MEMS simulator to extract efficiently the
surrogate models at the low level. In order to avoid the curse of
dimensionality, we employ tensor-train decomposition at the high level to
construct the basis functions and Gauss quadrature points. As a demonstration,
we verify our algorithm on a stochastic oscillator with four MEMS capacitors
and 184 random parameters. This challenging example is simulated efficiently by
our simulator at the cost of only 10 minutes in MATLAB on a regular personal
computer.Comment: 14 pages (IEEE double column), 11 figure, accepted by IEEE Trans CAD
of Integrated Circuits and System
Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems
In this paper we review basic and emerging models and associated algorithms
for large-scale tensor networks, especially Tensor Train (TT) decompositions
using novel mathematical and graphical representations. We discus the concept
of tensorization (i.e., creating very high-order tensors from lower-order
original data) and super compression of data achieved via quantized tensor
train (QTT) networks. The purpose of a tensorization and quantization is to
achieve, via low-rank tensor approximations "super" compression, and
meaningful, compact representation of structured data. The main objective of
this paper is to show how tensor networks can be used to solve a wide class of
big data optimization problems (that are far from tractable by classical
numerical methods) by applying tensorization and performing all operations
using relatively small size matrices and tensors and applying iteratively
optimized and approximative tensor contractions.
Keywords: Tensor networks, tensor train (TT) decompositions, matrix product
states (MPS), matrix product operators (MPO), basic tensor operations,
tensorization, distributed representation od data optimization problems for
very large-scale problems: generalized eigenvalue decomposition (GEVD),
PCA/SVD, canonical correlation analysis (CCA).Comment: arXiv admin note: text overlap with arXiv:1403.204
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