86,925 research outputs found
A Solution of the Strong CP Problem Transforming the theta-angle to the KM CP-violating Phase
It is shown that in the scheme with a rotating fermion mass matrix (i.e. one
with a scale-dependent orientation in generation space) suggested earlier for
explaining fermion mixing and mass hierarchy, the theta-angle term in the QCD
action of topological origin can be eliminated by chiral transformations, while
giving still nonzero masses to all quarks. Instead, the effects of such
transformations get transmitted by the rotation to the CKM matrix as the KM
phase giving, for of order unity, a Jarlskog invariant typically of
order as experimentally observed. Strong and weak CP violations
appear then as just two facets of the same phenomenon.Comment: 14 pages, 2 figure
Physics of heat pipe rewetting
Although several studies have been made to determine the rewetting characteristics of liquid films on heated rods, tubes, and flat plates, no solutions are yet available to describe the rewetting process of a hot plate subjected to a uniform heating. A model is presented to analyze the rewetting process of such plates with and without grooves. Approximate analytical solutions are presented for the prediction of the rewetting velocity and the transient temperature profiles of the plates. It is shown that the present rewetting velocity solution reduces correctly to the existing solution for the rewetting of an initially hot isothermal plate without heating from beneath the plate. Numerical solutions have also been obtained to validate the analytical solutions
Parametrization of stochastic inputs using generative adversarial networks with application in geology
We investigate artificial neural networks as a parametrization tool for
stochastic inputs in numerical simulations. We address parametrization from the
point of view of emulating the data generating process, instead of explicitly
constructing a parametric form to preserve predefined statistics of the data.
This is done by training a neural network to generate samples from the data
distribution using a recent deep learning technique called generative
adversarial networks. By emulating the data generating process, the relevant
statistics of the data are replicated. The method is assessed in subsurface
flow problems, where effective parametrization of underground properties such
as permeability is important due to the high dimensionality and presence of
high spatial correlations. We experiment with realizations of binary
channelized subsurface permeability and perform uncertainty quantification and
parameter estimation. Results show that the parametrization using generative
adversarial networks is very effective in preserving visual realism as well as
high order statistics of the flow responses, while achieving a dimensionality
reduction of two orders of magnitude
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