Results 51 to 60 of about 13,481 (226)
Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems
, 2014 Polynomial chaos expansions are used to reduce the computational cost in the Bayesian solutions of inverse problems by creating a surrogate posterior that can be evaluated inexpensively.
Chorin, Alexandre J. +3 more
core +1 more source
IEEE Access, 2019 Estimation of jitter and eye diagram in high-speed serial channels can be challenging. The existing methods might fail to show inter-symbol interference (ISI) and data dependent jitter because they are either excessively time consuming or only applicable
Majid Ahadi Dolatsara +3 more
doaj +1 more source
Polynomial chaos expansions for dependent random variables [PDF]
Computer Methods in Applied Mechanics and Engineering, 2019 John Jakeman +4 more
openaire +3 more sources
Variance-based sensitivity analysis of oil spill predictions in the Red Sea region
Frontiers in Marine Science, 2023 To support accidental spill rapid response efforts, oil spill simulations may generally need to account for uncertainties concerning the nature and properties of the spill, which compound those inherent in model parameterizations. A full detailed account
Mohamad Abed El Rahman Hammoud +4 more
doaj +1 more source
Compressive sensing adaptation for polynomial chaos expansions
, 2018 Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adaptations with different convergence properties.
Ghanem, Roger G. +7 more
core +1 more source
Magnetometric resistivity tomography using chaos polynomial expansion [PDF]
Geophysical Journal International, 2020 SUMMARY We present an inversion algorithm to reconstruct the spatial distribution of the electrical conductivity from the analysis of magnetometric resistivity (MMR) data acquired at the ground surface. We first review the theoretical background of MMR connecting the generation of a magnetic field in response to the injection of a low ...
Vu, M +3 more
openaire +3 more sources
On polynomial chaos expansion via gradient-enhanced ℓ1-minimization [PDF]
Journal of Computational Physics, 2016 Gradient-enhanced Uncertainty Quantification (UQ) has received recent attention, in which the derivatives of a Quantity of Interest (QoI) with respect to the uncertain parameters are utilized to improve the surrogate approximation. Polynomial chaos expansions (PCEs) are often employed in UQ, and when the QoI can be represented by a sparse PCE, $\ell_1$-
Peng, Ji +2 more
openaire +3 more sources
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Results in OpticsIn this work, we describe an algorithm based on Polynomial-Chaos (PC) expansions for the study of uncertainty quantification problems involving grating filters.
Aristeides D. Papadopoulos +3 more
doaj +1 more source
Stable Neural Signal Recording Processed by Memristor‐Based Reservoir Computing System
Advanced Intelligent Systems, EarlyView.This work introduces a memristor‐based reservoir computing (RC) system for real‐time, energy‐efficient processing of neural signals in brain‐machine interface (BMI). Combined with flexible mesh neural probes with tissue‐like flexibility and subcellular‐scale features that enable consistent, long‐term tracking of single‐cell neural activities, the ...
Soohyeon Kim +10 more
wiley +1 more source