Πλοήγηση ανά Θέμα "Inference engines"
Αποτελέσματα 1-20 από 36
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Adaptive Bayesian Inference Framework for Joint Model and Noise Identification
(2022)Model updating, the process of inferring a model from data, is prone to the adverse effects of modeling error, which is caused by simplification and idealization assumptions in the mathematical models. In this study, an ... -
Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis
(2020)Plant diseases are major threat to green product quality and agricultural productivity. Agronomists and farmers often encounter great difficulties in early detection of plant diseases and controlling their potential ... -
A Bayesian framework for calibration of multiaxial fatigue curves
(2022)A Bayesian framework is proposed to re-formulate a multiaxial fatigue model and produce probabilistic stress-life fatigue curves from experimental data. The proposed framework identifies the experimentally-driven parameters ... -
A bayesian framework for optimal experimental design in structural dynamics
(2016)A Bayesian framework for optimal experimental design in structural dynamics is presented. The optimal design is based on an expected utility function that measures the value of the information arising from alternative ... -
Bayesian identification of the tendon fascicle's structural composition using finite element models for helical geometries
(2017)Despite extensive experimental and computational investigations, the accurate determination of the structural composition of biological tendons remains elusive. Here we infer the structural compositions of tendons by ... -
Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme
(2020)Ultrasonic Guided Waves (GW) actuated by piezoelectric transducers installed on structures have proven to be sensitive to small structural defects, with acquired scattering signatures being dependent on the damage type. ... -
Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures
(2018)This paper presents a new framework for output-only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time-domain, using only the ... -
Bayesian optimal experimental design using asymptotic approximations
(2017)Bayesian optimal experimental design (OED) tools for model parameter estimation and response predictions in structural dynamics include sampling (Huan and Marzouk, J. Comput. Phys., 232:288–317, 2013) and asymptotic ... -
Bayesian optimal sensor placement for crack identification in structures using strain measurements
(2018)A Bayesian framework is presented for finding the optimal locations of strain sensors in a plate with a crack with the goal of identifying the crack properties, such as crack location, size, and orientation. Sensor grids ... -
Bayesian uncertainty quantification and propagation in molecular dynamics simulations
(2012)A comprehensive Bayesian probabilistic framework is developed for quantifying and calibrating the uncertainties in the parameters of the models (e.g. force-field potentials) involved in molecular dynamics (MD) simulations ... -
Bayesian uncertainty quantification and propagation in nonlinear structural dynamics
(2013)A Bayesian uncertainty quantification and propagation (UQ&P) framework is presented for identifying nonlinear models of dynamic systems using vibration measurements of their components. The measurements are taken to be ... -
Bayesian uncertainty quantification and propagation in nonlinear structural dynamics
(2009)Nonlinear modelling and parametric identification of an experimental vehicle model, are employed in this paper. The composite structure of the vehicle model is split into a frame substructure and to four support substructures. ... -
Bayesian uncertainty quantification and propagation using adjoint techniques
(2014)This paper presents the Bayesian inference framework enhanced by analytical approximations for uncertainty quantification and propagation and parameter estimation. A Gaussian distribution is used to approximate the posterior ... -
Bayesian uncertainty quantification of turbulence models based on high-order adjoint
(2015)The uncertainties in the parameters of turbulence models employed in computational fluid dynamics simulations are quantified using the Bayesian inference framework and analytical approximations. The posterior distribution ... -
A computationally efficient Bayesian framework for structural health monitoring using physics-based models
(2015)A Bayesian inference framework for structural damage identification is presented. Sophisticated structural identification methods, combining vibration information from the sensor network with the theoretical information ... -
Data features-based likelihood-informed Bayesian finite element model updating
(2019)A new formulation for likelihood-informed Bayesian inference is proposed in this work based on probability models introduced for the features between the measurements and model predictions. The formulation applies to both ... -
A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted metamodeling strategy and ultrasound measurements
(2020)Reliable verification and evaluation of the mechanical properties of a layered composite ensemble are critical for industrially relevant applications, however it still remains an open engineering challenge. In this study, ... -
A fast CMS technique for computational efficient system re-analyses in structural dynamics
(2009)Sensitivity analyses, model calibration techniques, uncertainty quantification methods, reliability computations and design optimization methods require a moderate to large number of system re-analyses to be performed for ... -
A fast CMS technique for computational efficient system re-analyses in structural dynamics
(2013)Sensitivity analyses, model calibration techniques, uncertainty quantification methods, reliability computations and design optimization methods require a moderate to large number of system re-analyses to be performed for ... -
Fast computing techniques for Bayesian uncertainty quantification in structural dynamics
(2013)A Bayesian probabilistic framework for uncertainty quantification and propagation in structural dynamics is reviewed. Fast computing techniques are integrated with the Bayesian framework to efficiently handle large-order ...