25,139 research outputs found
Computational fluid dynamics
An overview of computational fluid dynamics (CFD) activities at the Langley Research Center is given. The role of supercomputers in CFD research, algorithm development, multigrid approaches to computational fluid flows, aerodynamics computer programs, computational grid generation, turbulence research, and studies of rarefied gas flows are among the topics that are briefly surveyed
JDFTx: software for joint density-functional theory
Density-functional theory (DFT) has revolutionized computational prediction
of atomic-scale properties from first principles in physics, chemistry and
materials science. Continuing development of new methods is necessary for
accurate predictions of new classes of materials and properties, and for
connecting to nano- and mesoscale properties using coarse-grained theories.
JDFTx is a fully-featured open-source electronic DFT software designed
specifically to facilitate rapid development of new theories, models and
algorithms. Using an algebraic formulation as an abstraction layer, compact
C++11 code automatically performs well on diverse hardware including GPUs. This
code hosts the development of joint density-functional theory (JDFT) that
combines electronic DFT with classical DFT and continuum models of liquids for
first-principles calculations of solvated and electrochemical systems. In
addition, the modular nature of the code makes it easy to extend and interface
with, facilitating the development of multi-scale toolkits that connect to ab
initio calculations, e.g. photo-excited carrier dynamics combining electron and
phonon calculations with electromagnetic simulations.Comment: 9 pages, 3 figures, 2 code listing
Neural networks, error-correcting codes, and polynomials over the binary n-cube
Several ways of relating the concept of error-correcting codes to the concept of neural networks are presented. Performing maximum-likelihood decoding in a linear block error-correcting code is shown to be equivalent to finding a global maximum of the energy function of a certain neural network. Given a linear block code, a neural network can be constructed in such a way that every codeword corresponds to a local maximum. The connection between maximization of polynomials over the n-cube and error-correcting codes is also investigated; the results suggest that decoding techniques can be a useful tool for solving such maximization problems. The results are generalized to both nonbinary and nonlinear codes
Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE
In an effort to increase the versatility of finite element codes, we explore
the possibility of automatically creating the Jacobian matrix necessary for the
gradient-based solution of nonlinear systems of equations. Particularly, we aim
to assess the feasibility of employing the automatic differentiation tool
TAPENADE for this purpose on a large Fortran codebase that is the result of
many years of continuous development. As a starting point we will describe the
special structure of finite element codes and the implications that this code
design carries for an efficient calculation of the Jacobian matrix. We will
also propose a first approach towards improving the efficiency of such a
method. Finally, we will present a functioning method for the automatic
implementation of the Jacobian calculation in a finite element software, but
will also point out important shortcomings that will have to be addressed in
the future.Comment: 17 pages, 9 figure
Implementation of standard testbeds for numerical relativity
We discuss results that have been obtained from the implementation of the
initial round of testbeds for numerical relativity which was proposed in the
first paper of the Apples with Apples Alliance. We present benchmark results
for various codes which provide templates for analyzing the testbeds and to
draw conclusions about various features of the codes. This allows us to sharpen
the initial test specifications, design a new test and add theoretical insight.Comment: Corrected versio
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