Results 21 to 30 of about 252,868 (269)
Revisiting Bayesian Autoencoders With MCMC
IEEE Access, 2022 Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust ...
Rohitash Chandra +3 more
doaj +1 more source
Measurement uncertainty propagation in transistor model parameters via polynomial chaos expansion [PDF]
, 2017 We present an analysis of the propagation of measurement uncertainty in microwave transistor nonlinear models. As a case study, we focus on residual calibration uncertainty and its effect on modeled nonlinear capacitances extracted from small-signal ...
Avolio, Gustavo +6 more
core +2 more sources
Practical uncertainty reduction and quantification in shock physics measurements
Review of Scientific Instruments, 2015 We report the development of a simple error analysis sampling method for identifying intersections and inflection points to reduce total uncertainty in experimental data. This technique was used to reduce uncertainties in sound speed measurements by 80% over conventional methods.
M C, Akin, J H, Nguyen
openaire +3 more sources
STRUCTURING UNCERTAINTY MANAGEMENT FOR ENERGY SAVINGS CALCULATIONS
South African Journal of Industrial Engineering, 2019 South Africa has committed itself to reducing its greenhouse gas emissions. A key strategy to minimise the greenhouse gas intensity involves using incentivised energy efficiency initiatives.
Johnson, Kristin +2 more
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Parametrization of stochastic inputs using generative adversarial networks with application in geology [PDF]
, 2019 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 ...
Chan, Shing, Elsheikh, Ahmed H.
core +2 more sources
Journal of Modern Power Systems and Clean Energy, 2023 This letter develops a fast analytical method for uncertainty quantification of electromechanical oscillation frequency due to varying generator dampings.
Yongli Zhu, Chanan Singh
doaj +1 more source
AIP Advances, 2023 The determination of rate coefficient parameters in detailed chemical kinetic mechanisms through experiments often suffers from avoidable aleatory uncertainty, while the use of reduced mechanisms, based on various reduction methods, introduces epistemic ...
Linying Li, Bin Zhang, Hong Liu
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Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins [PDF]
, 2017 In this work we propose an Uncertainty Quantification methodology for sedimentary basins evolution under mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, non-linear, monodimensional (depth-only) system of PDEs
Colombo, Ivo +4 more
core +2 more sources
Energies, 2019 This work presents the Third-Order Adjoint Sensitivity Analysis Methodology (3rd-ASAM) for response-coupled forward and adjoint linear systems.
Dan Gabriel Cacuci
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Tensor Computation: A New Framework for High-Dimensional Problems in EDA [PDF]
, 2016 Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables.
Batselier, Kim +4 more
core +2 more sources