61,659 research outputs found
Advantages and challenges in coupling an ideal gas to atomistic models in adaptive resolution simulations
In adaptive resolution simulations, molecular fluids are modeled employing
different levels of resolution in different subregions of the system. When
traveling from one region to the other, particles change their resolution on
the fly. One of the main advantages of such approaches is the computational
efficiency gained in the coarse-grained region. In this respect the best
coarse-grained system to employ in the low resolution region would be the ideal
gas, making intermolecular force calculations in the coarse-grained subdomain
redundant. In this case, however, a smooth coupling is challenging due to the
high energetic imbalance between typical liquids and a system of
non-interacting particles. In the present work, we investigate this approach,
using as a test case the most biologically relevant fluid, water. We
demonstrate that a successful coupling of water to the ideal gas can be
achieved with current adaptive resolution methods, and discuss the issues that
remain to be addressed
Multi-scale uncertainty quantification in geostatistical seismic inversion
Geostatistical seismic inversion is commonly used to infer the spatial
distribution of the subsurface petro-elastic properties by perturbing the model
parameter space through iterative stochastic sequential
simulations/co-simulations. The spatial uncertainty of the inferred
petro-elastic properties is represented with the updated a posteriori variance
from an ensemble of the simulated realizations. Within this setting, the
large-scale geological (metaparameters) used to generate the petro-elastic
realizations, such as the spatial correlation model and the global a priori
distribution of the properties of interest, are assumed to be known and
stationary for the entire inversion domain. This assumption leads to
underestimation of the uncertainty associated with the inverted models. We
propose a practical framework to quantify uncertainty of the large-scale
geological parameters in seismic inversion. The framework couples
geostatistical seismic inversion with a stochastic adaptive sampling and
Bayesian inference of the metaparameters to provide a more accurate and
realistic prediction of uncertainty not restricted by heavy assumptions on
large-scale geological parameters. The proposed framework is illustrated with
both synthetic and real case studies. The results show the ability retrieve
more reliable acoustic impedance models with a more adequate uncertainty spread
when compared with conventional geostatistical seismic inversion techniques.
The proposed approach separately account for geological uncertainty at
large-scale (metaparameters) and local scale (trace-by-trace inversion)
Co-simulation of Continuous Systems: A Tutorial
Co-simulation consists of the theory and techniques to enable global
simulation of a coupled system via the composition of simulators. Despite the
large number of applications and growing interest in the challenges, the field
remains fragmented into multiple application domains, with limited sharing of
knowledge.
This tutorial aims at introducing co-simulation of continuous systems,
targeted at researchers new to the field
Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation
This paper presents a mutual coupling based calibration method for
time-division-duplex massive MIMO systems, which enables downlink precoding
based on uplink channel estimates. The entire calibration procedure is carried
out solely at the base station (BS) side by sounding all BS antenna pairs. An
Expectation-Maximization (EM) algorithm is derived, which processes the
measured channels in order to estimate calibration coefficients. The EM
algorithm outperforms current state-of-the-art narrow-band calibration schemes
in a mean squared error (MSE) and sum-rate capacity sense. Like its
predecessors, the EM algorithm is general in the sense that it is not only
suitable to calibrate a co-located massive MIMO BS, but also very suitable for
calibrating multiple BSs in distributed MIMO systems.
The proposed method is validated with experimental evidence obtained from a
massive MIMO testbed. In addition, we address the estimated narrow-band
calibration coefficients as a stochastic process across frequency, and study
the subspace of this process based on measurement data. With the insights of
this study, we propose an estimator which exploits the structure of the process
in order to reduce the calibration error across frequency. A model for the
calibration error is also proposed based on the asymptotic properties of the
estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications,
21/Feb/201
Integration of continuous-time dynamics in a spiking neural network simulator
Contemporary modeling approaches to the dynamics of neural networks consider
two main classes of models: biologically grounded spiking neurons and
functionally inspired rate-based units. The unified simulation framework
presented here supports the combination of the two for multi-scale modeling
approaches, the quantitative validation of mean-field approaches by spiking
network simulations, and an increase in reliability by usage of the same
simulation code and the same network model specifications for both model
classes. While most efficient spiking simulations rely on the communication of
discrete events, rate models require time-continuous interactions between
neurons. Exploiting the conceptual similarity to the inclusion of gap junctions
in spiking network simulations, we arrive at a reference implementation of
instantaneous and delayed interactions between rate-based models in a spiking
network simulator. The separation of rate dynamics from the general connection
and communication infrastructure ensures flexibility of the framework. We
further demonstrate the broad applicability of the framework by considering
various examples from the literature ranging from random networks to neural
field models. The study provides the prerequisite for interactions between
rate-based and spiking models in a joint simulation
Design of Dispersive Delay Structures (DDSs) Formed by Coupled C-Sections Using Predistortion with Space Mapping
The concept of space mapping is applied, for the first time, to the design of
microwave dispersive delay structures (DDSs). DDSs are components providing
specified group delay versus frequency responses for real-time radio systems.
The DDSs considered in this paper are formed by cascaded coupled C-sections. It
is first shown that aggressive space mapping does not provide sufficient
accuracy in the synthesis of DDSs. To address this issue, we propose a
predistortion space mapping technique. Compared to aggressive space mapping,
this technique provides enhanced accuracy, while compared to output space
mapping, it provides greater implementation simplicity. Two full-wave and one
experimental examples are provided to illustrate the proposed predistortion
space mapping technique
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