48,164 research outputs found
A Stochastic Multi-scale Approach for Numerical Modeling of Complex Materials - Application to Uniaxial Cyclic Response of Concrete
In complex materials, numerous intertwined phenomena underlie the overall
response at macroscale. These phenomena can pertain to different engineering
fields (mechanical , chemical, electrical), occur at different scales, can
appear as uncertain, and are nonlinear. Interacting with complex materials thus
calls for developing nonlinear computational approaches where multi-scale
techniques that grasp key phenomena at the relevant scale need to be mingled
with stochastic methods accounting for uncertainties. In this chapter, we
develop such a computational approach for modeling the mechanical response of a
representative volume of concrete in uniaxial cyclic loading. A mesoscale is
defined such that it represents an equivalent heterogeneous medium: nonlinear
local response is modeled in the framework of Thermodynamics with Internal
Variables; spatial variability of the local response is represented by
correlated random vector fields generated with the Spectral Representation
Method. Macroscale response is recovered through standard ho-mogenization
procedure from Micromechanics and shows salient features of the uniaxial cyclic
response of concrete that are not explicitly modeled at mesoscale.Comment: Computational Methods for Solids and Fluids, 41, Springer
International Publishing, pp.123-160, 2016, Computational Methods in Applied
Sciences, 978-3-319-27994-
KInNeSS: A Modular Framework for Computational Neuroscience
Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Advancing the size and complexity of neural network models leads to an ever
increasing demand for computational resources for their simulation.
Neuromorphic devices offer a number of advantages over conventional computing
architectures, such as high emulation speed or low power consumption, but this
usually comes at the price of reduced configurability and precision. In this
article, we investigate the consequences of several such factors that are
common to neuromorphic devices, more specifically limited hardware resources,
limited parameter configurability and parameter variations. Our final aim is to
provide an array of methods for coping with such inevitable distortion
mechanisms. As a platform for testing our proposed strategies, we use an
executable system specification (ESS) of the BrainScaleS neuromorphic system,
which has been designed as a universal emulation back-end for neuroscientific
modeling. We address the most essential limitations of this device in detail
and study their effects on three prototypical benchmark network models within a
well-defined, systematic workflow. For each network model, we start by defining
quantifiable functionality measures by which we then assess the effects of
typical hardware-specific distortion mechanisms, both in idealized software
simulations and on the ESS. For those effects that cause unacceptable
deviations from the original network dynamics, we suggest generic compensation
mechanisms and demonstrate their effectiveness. Both the suggested workflow and
the investigated compensation mechanisms are largely back-end independent and
do not require additional hardware configurability beyond the one required to
emulate the benchmark networks in the first place. We hereby provide a generic
methodological environment for configurable neuromorphic devices that are
targeted at emulating large-scale, functional neural networks
Diffusion quantum Monte Carlo study of three-dimensional Wigner crystals
We report diffusion quantum Monte Carlo calculations of three-dimensional
Wigner crystals in the density range r_s=100-150. We have tested different
types of orbital for use in the approximate wave functions but none improve
upon the simple Gaussian form. The Gaussian exponents are optimized by directly
minimizing the diffusion quantum Monte Carlo energy. We have carefully
investigated and sought to minimize the potential biases in our Monte Carlo
results. We conclude that the uniform electron gas undergoes a transition from
a ferromagnetic fluid to a body-centered-cubic Wigner crystal at r_s=106+/-1.
The diffusion quantum Monte Carlo results are compared with those from
Hartree-Fock and Hartree theory in order to understand the role played by
exchange and correlation in Wigner crystals. We also study "floating" Wigner
crystals and give results for their pair-correlation functions
On the Statistics of Reaction-Diffusion Simulations for Molecular Communication
A molecule traveling in a realistic propagation environment can experience
stochastic interactions with other molecules and the environment boundary. The
statistical behavior of some isolated phenomena, such as dilute unbounded
molecular diffusion, are well understood. However, the coupling of multiple
interactions can impede closed-form analysis, such that simulations are
required to determine the statistics. This paper compares the statistics of
molecular reaction-diffusion simulation models from the perspective of
molecular communication systems. Microscopic methods track the location and
state of every molecule, whereas mesoscopic methods partition the environment
into virtual containers that hold molecules. The properties of each model are
described and compared with a hybrid of both models. Simulation results also
assess the accuracy of Poisson and Gaussian approximations of the underlying
Binomial statistics.Comment: 6 pages, 1 table, 10 figures. Submitted to the 2nd ACM International
Conference on Nanoscale Computing and Communication (ACM NANOCOM 2015) on May
16, 201
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