40,333 research outputs found
Continuum variational and diffusion quantum Monte Carlo calculations
This topical review describes the methodology of continuum variational and
diffusion quantum Monte Carlo calculations. These stochastic methods are based
on many-body wave functions and are capable of achieving very high accuracy.
The algorithms are intrinsically parallel and well-suited to petascale
computers, and the computational cost scales as a polynomial of the number of
particles. A guide to the systems and topics which have been investigated using
these methods is given. The bulk of the article is devoted to an overview of
the basic quantum Monte Carlo methods, the forms and optimisation of wave
functions, performing calculations within periodic boundary conditions, using
pseudopotentials, excited-state calculations, sources of calculational
inaccuracy, and calculating energy differences and forces
Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders
We introduce two new loss functions designed to directly optimise the
statistical significance of the expected number of signal events when training
neural networks to classify events as signal or background in the scenario of a
search for new physics at a particle collider. The loss functions are designed
to directly maximise commonly used estimates of the statistical significance,
, and the Asimov estimate, . We consider their use in a toy
SUSY search with 30~fb of 14~TeV data collected at the LHC. In the case
that the search for the SUSY model is dominated by systematic uncertainties, it
is found that the loss function based on can outperform the binary cross
entropy in defining an optimal search region
A metamodel based optimisation algorithm for metal forming processes
Cost saving and product improvement have always been important goals in the metal\ud
forming industry. To achieve these goals, metal forming processes need to be optimised. During\ud
the last decades, simulation software based on the Finite Element Method (FEM) has significantly\ud
contributed to designing feasible processes more easily. More recently, the possibility of\ud
coupling FEM to mathematical optimisation algorithms is offering a very promising opportunity\ud
to design optimal metal forming processes instead of only feasible ones. However, which\ud
optimisation algorithm to use is still not clear.\ud
In this paper, an optimisation algorithm based on metamodelling techniques is proposed\ud
for optimising metal forming processes. The algorithm incorporates nonlinear FEM simulations\ud
which can be very time consuming to execute. As an illustration of its capabilities, the\ud
proposed algorithm is applied to optimise the internal pressure and axial feeding load paths\ud
of a hydroforming process. The product formed by the optimised process outperforms products\ud
produced by other, arbitrarily selected load paths. These results indicate the high potential of\ud
the proposed algorithm for optimising metal forming processes using time consuming FEM\ud
simulations
Testing an Optimised Expansion on Z_2 Lattice Models
We test an optimised hopping parameter expansion on various Z_2 lattice
scalar field models: the Ising model, a spin-one model and lambda (phi)^4. We
do this by studying the critical indices for a variety of optimisation
criteria, in a range of dimensions and with various trial actions. We work up
to seventh order, thus going well beyond previous studies. We demonstrate how
to use numerical methods to generate the high order diagrams and their
corresponding expressions. These are then used to calculate results numerically
and, in the case of the Ising model, we obtain some analytic results. We
highlight problems with several optimisation schemes and show for the best
scheme that the critical exponents are consistent with mean field results to at
least 8 significant figures. We conclude that in its present form, such
optimised lattice expansions do not seem to be capturing the non-perturbative
infra-red physics near the critical points of scalar models.Comment: 47 pages, some figures in colour but will display fine in B
Solving optimisation problems in metal forming using Finite Element simulation and metamodelling techniques
During the last decades, Finite Element (FEM) simulations\ud
of metal forming processes have become important\ud
tools for designing feasible production processes. In more\ud
recent years, several authors recognised the potential of\ud
coupling FEM simulations to mathematical optimisation\ud
algorithms to design optimal metal forming processes instead\ud
of only feasible ones.\ud
Within the current project, an optimisation strategy is being\ud
developed, which is capable of optimising metal forming\ud
processes in general using time consuming nonlinear\ud
FEM simulations. The expression “optimisation strategy”\ud
is used to emphasise that the focus is not solely on solving\ud
optimisation problems by an optimisation algorithm, but\ud
the way these optimisation problems in metal forming are\ud
modelled is also investigated. This modelling comprises\ud
the quantification of objective functions and constraints\ud
and the selection of design variables.\ud
This paper, however, is concerned with the choice for\ud
and the implementation of an optimisation algorithm for\ud
solving optimisation problems in metal forming. Several\ud
groups of optimisation algorithms can be encountered in\ud
metal forming literature: classical iterative, genetic and\ud
approximate optimisation algorithms are already applied\ud
in the field. We propose a metamodel based optimisation\ud
algorithm belonging to the latter group, since approximate\ud
algorithms are relatively efficient in case of time consuming\ud
function evaluations such as the nonlinear FEM calculations\ud
we are considering. Additionally, approximate optimisation\ud
algorithms strive for a global optimum and do\ud
not need sensitivities, which are quite difficult to obtain\ud
for FEM simulations. A final advantage of approximate\ud
optimisation algorithms is the process knowledge, which\ud
can be gained by visualising metamodels.\ud
In this paper, we propose a sequential approximate optimisation\ud
algorithm, which incorporates both Response\ud
Surface Methodology (RSM) and Design and Analysis\ud
of Computer Experiments (DACE) metamodelling techniques.\ud
RSM is based on fitting lower order polynomials\ud
by least squares regression, whereas DACE uses Kriging\ud
interpolation functions as metamodels. Most authors in\ud
the field of metal forming use RSM, although this metamodelling\ud
technique was originally developed for physical\ud
experiments that are known to have a stochastic na-\ud
¤Faculty of Engineering Technology (Applied Mechanics group),\ud
University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands,\ud
email: [email protected]\ud
ture due to measurement noise present. This measurement\ud
noise is absent in case of deterministic computer experiments\ud
such as FEM simulations. Hence, an interpolation\ud
model fitted by DACE is thought to be more applicable in\ud
combination with metal forming simulations. Nevertheless,\ud
the proposed algorithm utilises both RSM and DACE\ud
metamodelling techniques.\ud
As a Design Of Experiments (DOE) strategy, a combination\ud
of a maximin spacefilling Latin Hypercubes Design\ud
and a full factorial design was implemented, which takes\ud
into account explicit constraints. Additionally, the algorithm\ud
incorporates cross validation as a metamodel validation\ud
technique and uses a Sequential Quadratic Programming\ud
algorithm for metamodel optimisation. To overcome\ud
the problem of ending up in a local optimum, the\ud
SQP algorithm is initialised from every DOE point, which\ud
is very time efficient since evaluating the metamodels can\ud
be done within a fraction of a second. The proposed algorithm\ud
allows for sequential improvement of the metamodels\ud
to obtain a more accurate optimum.\ud
As an example case, the optimisation algorithm was applied\ud
to obtain the optimised internal pressure and axial\ud
feeding load paths to minimise wall thickness variations\ud
in a simple hydroformed product. The results are satisfactory,\ud
which shows the good applicability of metamodelling\ud
techniques to optimise metal forming processes using\ud
time consuming FEM simulations
Training and Scaling Preference Functions for Disambiguation
We present an automatic method for weighting the contributions of preference
functions used in disambiguation. Initial scaling factors are derived as the
solution to a least-squares minimization problem, and improvements are then
made by hill-climbing. The method is applied to disambiguating sentences in the
ATIS (Air Travel Information System) corpus, and the performance of the
resulting scaling factors is compared with hand-tuned factors. We then focus on
one class of preference function, those based on semantic lexical collocations.
Experimental results are presented showing that such functions vary
considerably in selecting correct analyses. In particular we define a function
that performs significantly better than ones based on mutual information and
likelihood ratios of lexical associations.Comment: To appear in Computational Linguistics (probably volume 20, December
94). LaTeX, 21 page
Forward Flux Sampling for rare event simulations
Rare events are ubiquitous in many different fields, yet they are notoriously
difficult to simulate because few, if any, events are observed in a conventiona
l simulation run. Over the past several decades, specialised simulation methods
have been developed to overcome this problem. We review one recently-developed
class of such methods, known as Forward Flux Sampling. Forward Flux Sampling
uses a series of interfaces between the initial and final states to calculate
rate constants and generate transition paths, for rare events in equilibrium or
nonequilibrium systems with stochastic dynamics. This review draws together a
number of recent advances, summarizes several applications of the method and
highlights challenges that remain to be overcome.Comment: minor typos in the manuscript. J.Phys.:Condensed Matter (accepted for
publication
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