16,978 research outputs found
Fleet Prognosis with Physics-informed Recurrent Neural Networks
Services and warranties of large fleets of engineering assets is a very
profitable business. The success of companies in that area is often related to
predictive maintenance driven by advanced analytics. Therefore, accurate
modeling, as a way to understand how the complex interactions between operating
conditions and component capability define useful life, is key for services
profitability. Unfortunately, building prognosis models for large fleets is a
daunting task as factors such as duty cycle variation, harsh environments,
inadequate maintenance, and problems with mass production can lead to large
discrepancies between designed and observed useful lives. This paper introduces
a novel physics-informed neural network approach to prognosis by extending
recurrent neural networks to cumulative damage models. We propose a new
recurrent neural network cell designed to merge physics-informed and
data-driven layers. With that, engineers and scientists have the chance to use
physics-informed layers to model parts that are well understood (e.g., fatigue
crack growth) and use data-driven layers to model parts that are poorly
characterized (e.g., internal loads). A simple numerical experiment is used to
present the main features of the proposed physics-informed recurrent neural
network for damage accumulation. The test problem consist of predicting fatigue
crack length for a synthetic fleet of airplanes subject to different mission
mixes. The model is trained using full observation inputs (far-field loads) and
very limited observation of outputs (crack length at inspection for only a
portion of the fleet). The results demonstrate that our proposed hybrid
physics-informed recurrent neural network is able to accurately model fatigue
crack growth even when the observed distribution of crack length does not match
with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer
perceptron, the stress intensity and Paris law layers, the cumulative damage
cell, as well as python driver scripts) used in this manuscript are publicly
available on GitHub at https://github.com/PML-UCF/pinn. The data and code are
released under the MIT Licens
A coherent state approach to effective potential in noncommutative D=(2+1) models
In this work we study the effective potential in noncommutative
three-dimensional models where the noncommutativity is introduced through the
coherent state approach. We discuss some important characteristics that seem to
be typical to this approach, specially the behavior of the quantum corrections
in the small noncommutativity limit.Comment: revtex4, 8 pages, 2 figures
Spatial and spin symmetry breaking in semidefinite-programming-based Hartree-Fock theory
The Hartree-Fock problem was recently recast as a semidefinite optimization
over the space of rank-constrained two-body reduced-density matrices (RDMs)
[Phys. Rev. A 89, 010502(R) (2014)]. This formulation of the problem transfers
the non-convexity of the Hartree-Fock energy functional to the rank constraint
on the two-body RDM. We consider an equivalent optimization over the space of
positive semidefinite one-electron RDMs (1-RDMs) that retains the non-convexity
of the Hartree-Fock energy expression. The optimized 1-RDM satisfies ensemble
-representability conditions, and ensemble spin-state conditions may be
imposed as well. The spin-state conditions place additional linear and
nonlinear constraints on the 1-RDM. We apply this RDM-based approach to several
molecular systems and explore its spatial (point group) and spin ( and
) symmetry breaking properties. When imposing and symmetry but
relaxing point group symmetry, the procedure often locates
spatial-symmetry-broken solutions that are difficult to identify using standard
algorithms. For example, the RDM-based approach yields a smooth,
spatial-symmetry-broken potential energy curve for the well-known Be--H
insertion pathway. We also demonstrate numerically that, upon relaxation of
and symmetry constraints, the RDM-based approach is equivalent to
real-valued generalized Hartree-Fock theory.Comment: 9 pages, 6 figure
A Fault Analytic Method against HB+
The search for lightweight authentication protocols suitable for low-cost
RFID tags constitutes an active and challenging research area. In this context,
a family of protocols based on the LPN problem has been proposed: the so-called
HB-family. Despite the rich literature regarding the cryptanalysis of these
protocols, there are no published results about the impact of fault analysis
over them. The purpose of this paper is to fill this gap by presenting a fault
analytic method against a prominent member of the HB-family: HB+ protocol. We
demonstrate that the fault analysis model can lead to a flexible and effective
attack against HB-like protocols, posing a serious threat over them
On the Adler-Bell-Jackiw anomaly in a Horava-Lifshitz-like QED
We show the absence of the ABJ anomaly for the Horava-Lifshitz-like QED with
any even . Besides of this, we study the graph contributing to the ABJ
anomaly at non-zero temperature and extend the Fujikawa's methodology of
studying the integral measure for our model.Comment: 9 pages, version accepted to EP
On the perturbative generation of the higher-derivative Lorentz-breaking terms
In this paper, we describe the perturbative generation of the
higher-derivative Lorentz-breaking terms for the gauge field, that is, the
Myers-Pospelov term and the higher-derivative Carroll-Field-Jackiw term. These
terms are explicitly calculated in the one-loop approximation and shown to be
finite and ambiguous.Comment: 12 pages, version accepted to PR
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