265 research outputs found
A Parallel General Purpose Multi-Objective Optimization Framework, with Application to Beam Dynamics
Particle accelerators are invaluable tools for research in the basic and
applied sciences, in fields such as materials science, chemistry, the
biosciences, particle physics, nuclear physics and medicine. The design,
commissioning, and operation of accelerator facilities is a non-trivial task,
due to the large number of control parameters and the complex interplay of
several conflicting design goals. We propose to tackle this problem by means of
multi-objective optimization algorithms which also facilitate a parallel
deployment. In order to compute solutions in a meaningful time frame a fast and
scalable software framework is required. In this paper, we present the
implementation of such a general-purpose framework for simulation-based
multi-objective optimization methods that allows the automatic investigation of
optimal sets of machine parameters. The implementation is based on a
master/slave paradigm, employing several masters that govern a set of slaves
executing simulations and performing optimization tasks. Using evolutionary
algorithms as the optimizer and OPAL as the forward solver, validation
experiments and results of multi-objective optimization problems in the domain
of beam dynamics are presented. The high charge beam line at the Argonne
Wakefield Accelerator Facility was used as the beam dynamics model. The 3D beam
size, transverse momentum, and energy spread were optimized
Solar radiation forecasting using ad-hoc time series preprocessing and neural networks
In this paper, we present an application of neural networks in the renewable
energy domain. We have developed a methodology for the daily prediction of
global solar radiation on a horizontal surface. We use an ad-hoc time series
preprocessing and a Multi-Layer Perceptron (MLP) in order to predict solar
radiation at daily horizon. First results are promising with nRMSE < 21% and
RMSE < 998 Wh/m2. Our optimized MLP presents prediction similar to or even
better than conventional methods such as ARIMA techniques, Bayesian inference,
Markov chains and k-Nearest-Neighbors approximators. Moreover we found that our
data preprocessing approach can reduce significantly forecasting errors.Comment: 14 pages, 8 figures, 2009 International Conference on Intelligent
Computin
A Fast Parallel Poisson Solver on Irregular Domains Applied to Beam Dynamic Simulations
We discuss the scalable parallel solution of the Poisson equation within a
Particle-In-Cell (PIC) code for the simulation of electron beams in particle
accelerators of irregular shape. The problem is discretized by Finite
Differences. Depending on the treatment of the Dirichlet boundary the resulting
system of equations is symmetric or `mildly' nonsymmetric positive definite. In
all cases, the system is solved by the preconditioned conjugate gradient
algorithm with smoothed aggregation (SA) based algebraic multigrid (AMG)
preconditioning. We investigate variants of the implementation of SA-AMG that
lead to considerable improvements in the execution times. We demonstrate good
scalability of the solver on distributed memory parallel processor with up to
2048 processors. We also compare our SAAMG-PCG solver with an FFT-based solver
that is more commonly used for applications in beam dynamics
An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle
Mantle convection is the fundamental physical process within earth's interior responsible for the thermal and geological evolution of the planet, including plate tectonics. The mantle is modeled as a viscous, incompressible, non-Newtonian fluid. The wide range of spatial scales, extreme variability and anisotropy in material properties, and severely nonlinear rheology have made global mantle convection modeling with realistic parameters prohibitive. Here we present a new implicit solver that exhibits optimal algorithmic performance and is capable of extreme scaling for hard PDE problems, such as mantle convection. To maximize accuracy and minimize runtime, the solver incorporates a number of advances, including aggressive multi-octree adaptivity, mixed continuous-discontinuous discretization, arbitrarily-high-order accuracy, hybrid spectral/geometric/algebraic multigrid, and novel Schur-complement preconditioning. These features present enormous challenges for extreme scalability. We demonstrate that---contrary to conventional wisdom---algorithmically optimal implicit solvers can be designed that scale out to 1.5 million cores for severely nonlinear, ill-conditioned, heterogeneous, and anisotropic PDEs
Quark mean field model with density dependent couplings for finite nuclei
The quark mean field model, which describes the nucleon using the constituent
quark model, is applied to investigate the properties of finite nuclei. The
couplings of the scalar and vector mesons with quarks are made density
dependent through direct coupling to the scalar field so as to reproduce the
relativistic Brueckner-Hartree-Fock results of nuclear matter. The present
model provides satisfactory results on the properties of spherical nuclei, and
predicts an increasing size of the nucleon as well as a reduction of the
nucleon mass in the nuclear environmentComment: 8 pages, REVTeX, 8 ps figures, accepted for publication in Phys. Rev.
Neutron star properties with relativistic equations of state
We study the properties of neutron stars adopting relativistic equations of
state of neutron star matter, calculated in the framework of the relativistic
Brueckner-Hartree-Fock approximation for electrically charge neutral neutron
star matter in beta-equilibrium. For higher densities more baryons (hyperons
etc.) are included by means of the relativistic Hartree- or Hartree-Fock
approximation. The special features of the different approximations and
compositions are discussed in detail. Besides standard neutron star properties
special emphasis is put on the limiting periods of neutron stars, for which the
Kepler criterion and gravitation-reaction instabilities are considered.
Furthermore the cooling behaviour of neutron stars is investigated, too. For
comparison we give also the outcome for some nonrelativistic equations of
state.Comment: 43 pages, 22 ps-figures, to be published in the International Journal
of Modern Physics
Deflating the deep brain stimulation causes personality changes bubble: the authors reply
To conclude that there is enough or not enough evidence demonstrating that deep brain stimulation (DBS) causes unintended postoperative personality changes is an epistemic problem that should be answered on the basis of established, replicable, and valid data. If prospective DBS recipients delay or refuse to be implanted because they are afraid of suffering from personality changes following DBS, and their fears are based on unsubstantiated claims made in the neuroethics literature, then researchers making these claims bear great responsibility for prospective recipients' medical decisions and subsequent well-being. Our article “Deflating the ‘DBS causes personality’ bubble” reported an increase in theoretical neuroethics publications suggesting putative DBS-induced changes to personality, identity, agency, autonomy, authenticity and/or self (PIAAAS) and a critical lack of supporting primary empirical studies. This special issue of Neuroethics brings together responses to our initial publication, with our own counter-responses organized according to common themes. We provide a brief summary for each commentary and its main criticisms as well as a discussion of the way in which these responses can: 1) help clarify the meaning of PIAAAS, suggesting supplementary frameworks for understanding the impact of DBS on PIAAAS; 2) provide further empirical evidence of PIAAAS by presenting results from the researchers’ own work; and/or 3) offer a critique of our research approach and/or findings. Unintended postoperative putative changes to PIAAAS remain a critical ethical concern. It is beyond dispute that we need to develop reliable empirical and conceptual instruments able to measure complex cognitive, affective, and behavioural changes in order to investigate whether they are attributable to DBS alone
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