168,517 research outputs found
An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition
Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data
Memory embedded non-intrusive reduced order modeling of non-ergodic flows
Generating a digital twin of any complex system requires modeling and
computational approaches that are efficient, accurate, and modular. Traditional
reduced order modeling techniques are targeted at only the first two but the
novel non-intrusive approach presented in this study is an attempt at taking
all three into account effectively compared to their traditional counterparts.
Based on dimensionality reduction using proper orthogonal decomposition (POD),
we introduce a long short-term memory (LSTM) neural network architecture
together with a principal interval decomposition (PID) framework as an enabler
to account for localized modal deformation, which is a key element in accurate
reduced order modeling of convective flows. Our applications for convection
dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations
demonstrate that the proposed approach yields significantly more accurate
predictions than the POD-Galerkin method, and could be a key enabler towards
near real-time predictions of unsteady flows
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
Networks are a unifying framework for modeling complex systems and network
inference problems are frequently encountered in many fields. Here, I develop
and apply a generative approach to network inference (RCweb) for the case when
the network is sparse and the latent (not observed) variables affect the
observed ones. From all possible factor analysis (FA) decompositions explaining
the variance in the data, RCweb selects the FA decomposition that is consistent
with a sparse underlying network. The sparsity constraint is imposed by a novel
method that significantly outperforms (in terms of accuracy, robustness to
noise, complexity scaling, and computational efficiency) Bayesian methods and
MLE methods using l1 norm relaxation such as K-SVD and l1--based sparse
principle component analysis (PCA). Results from simulated models demonstrate
that RCweb recovers exactly the model structures for sparsity as low (as
non-sparse) as 50% and with ratio of unobserved to observed variables as high
as 2. RCweb is robust to noise, with gradual decrease in the parameter ranges
as the noise level increases.Comment: 8 pages, 5 figure
BlenX-based compositional modeling of complex reaction mechanisms
Molecular interactions are wired in a fascinating way resulting in complex
behavior of biological systems. Theoretical modeling provides a useful
framework for understanding the dynamics and the function of such networks. The
complexity of the biological networks calls for conceptual tools that manage
the combinatorial explosion of the set of possible interactions. A suitable
conceptual tool to attack complexity is compositionality, already successfully
used in the process algebra field to model computer systems. We rely on the
BlenX programming language, originated by the beta-binders process calculus, to
specify and simulate high-level descriptions of biological circuits. The
Gillespie's stochastic framework of BlenX requires the decomposition of
phenomenological functions into basic elementary reactions. Systematic
unpacking of complex reaction mechanisms into BlenX templates is shown in this
study. The estimation/derivation of missing parameters and the challenges
emerging from compositional model building in stochastic process algebras are
discussed. A biological example on circadian clock is presented as a case study
of BlenX compositionality
Nonintrusive Uncertainty Quantification for automotive crash problems with VPS/Pamcrash
Uncertainty Quantification (UQ) is a key discipline for computational
modeling of complex systems, enhancing reliability of engineering simulations.
In crashworthiness, having an accurate assessment of the behavior of the model
uncertainty allows reducing the number of prototypes and associated costs.
Carrying out UQ in this framework is especially challenging because it requires
highly expensive simulations. In this context, surrogate models (metamodels)
allow drastically reducing the computational cost of Monte Carlo process.
Different techniques to describe the metamodel are considered, Ordinary
Kriging, Polynomial Response Surfaces and a novel strategy (based on Proper
Generalized Decomposition) denoted by Separated Response Surface (SRS). A large
number of uncertain input parameters may jeopardize the efficiency of the
metamodels. Thus, previous to define a metamodel, kernel Principal Component
Analysis (kPCA) is found to be effective to simplify the model outcome
description. A benchmark crash test is used to show the efficiency of combining
metamodels with kPCA
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
In this paper, we put forth a long short-term memory (LSTM) nudging framework
for the enhancement of reduced order models (ROMs) of fluid flows utilizing
noisy measurements. We build on the fact that in a realistic application, there
are uncertainties in initial conditions, boundary conditions, model parameters,
and/or field measurements. Moreover, conventional nonlinear ROMs based on
Galerkin projection (GROMs) suffer from imperfection and solution instabilities
due to the modal truncation, especially for advection-dominated flows with slow
decay in the Kolmogorov width. In the presented LSTM-Nudge approach, we fuse
forecasts from a combination of imperfect GROM and uncertain state estimates,
with sparse Eulerian sensor measurements to provide more reliable predictions
in a dynamical data assimilation framework. We illustrate the idea with the
viscous Burgers problem, as a benchmark test bed with quadratic nonlinearity
and Laplacian dissipation. We investigate the effects of measurements noise and
state estimate uncertainty on the performance of the LSTM-Nudge behavior. We
also demonstrate that it can sufficiently handle different levels of temporal
and spatial measurement sparsity. This first step in our assessment of the
proposed model shows that the LSTM nudging could represent a viable realtime
predictive tool in emerging digital twin systems
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