54,483 research outputs found
Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model
Multi-agent geographical models integrate very large numbers of spatial
interactions. In order to validate those models large amount of computing is
necessary for their simulation and calibration. Here a new data processing
chain including an automated calibration procedure is experimented on a
computational grid using evolutionary algorithms. This is applied for the first
time to a geographical model designed to simulate the evolution of an early
urban settlement system. The method enables us to reduce the computing time and
provides robust results. Using this method, we identify several parameter
settings that minimise three objective functions that quantify how closely the
model results match a reference pattern. As the values of each parameter in
different settings are very close, this estimation considerably reduces the
initial possible domain of variation of the parameters. The model is thus a
useful tool for further multiple applications on empirical historical
situations
Component-based simulation for a reconfiguration study of transitic systems
This paper is organized as follows. Part A presents the context of reconfiguring transitic systems and the main idea in implementing the decision step. It comprises sections 1 to 3. Section 3 presents an example that illustrates the concepts presented in the next sections. Parts B and C express the models and principles used to simulate transitic systems, the result of which will be helpful for choosing the new configuration. Part B focuses mainly on models. It comprises sections 4 to 6. Part C focuses mainly on simulation principles. It comprises sections 7 to 10
What is a quantum simulator?
Quantum simulators are devices that actively use quantum effects to answer
questions about model systems and, through them, real systems. Here we expand
on this definition by answering several fundamental questions about the nature
and use of quantum simulators. Our answers address two important areas. First,
the difference between an operation termed simulation and another termed
computation. This distinction is related to the purpose of an operation, as
well as our confidence in and expectation of its accuracy. Second, the
threshold between quantum and classical simulations. Throughout, we provide a
perspective on the achievements and directions of the field of quantum
simulation.Comment: 13 pages, 2 figure
Simulating chemistry efficiently on fault-tolerant quantum computers
Quantum computers can in principle simulate quantum physics exponentially
faster than their classical counterparts, but some technical hurdles remain.
Here we consider methods to make proposed chemical simulation algorithms
computationally fast on fault-tolerant quantum computers in the circuit model.
Fault tolerance constrains the choice of available gates, so that arbitrary
gates required for a simulation algorithm must be constructed from sequences of
fundamental operations. We examine techniques for constructing arbitrary gates
which perform substantially faster than circuits based on the conventional
Solovay-Kitaev algorithm [C.M. Dawson and M.A. Nielsen, \emph{Quantum Inf.
Comput.}, \textbf{6}:81, 2006]. For a given approximation error ,
arbitrary single-qubit gates can be produced fault-tolerantly and using a
limited set of gates in time which is or ; with sufficient parallel preparation of ancillas, constant average
depth is possible using a method we call programmable ancilla rotations.
Moreover, we construct and analyze efficient implementations of first- and
second-quantized simulation algorithms using the fault-tolerant arbitrary gates
and other techniques, such as implementing various subroutines in constant
time. A specific example we analyze is the ground-state energy calculation for
Lithium hydride.Comment: 33 pages, 18 figure
Multidisciplinary computational aerosciences
As the challenges of single disciplinary computational physics are met, such as computational fluid dynamics, computational structural mechanics, computational propulsion, computational aeroacoustics, computational electromagnetics, etc., scientists have begun investigating the combination of these single disciplines into what is being called multidisciplinary computational aerosciences (MCAS). The combination of several disciplines not only offers simulation realism but also formidable computational challenges. The solution of such problems will require computers orders of magnitude larger than those currently available. Such computer power can only be supplied by massively parallel machines because of the current speed-of-light limitation of conventional serial systems. Even with such machines, MCAS problems will require hundreds of hours for their solution. To efficiently utilize such a machine, research is required in three areas that include parallel architectures, systems software, and applications software. The main emphasis of this paper is the applications software element. Examples that demonstrate application software for multidisciplinary problems currently being solved at NASA Ames Research Center are presented. Pacing items for MCAS are discussed such as solution methodology, physical modeling, computer power, and multidisciplinary validation experiments
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