Results 71 to 80 of about 13,481 (226)
Mathematical and Computational Applications, 2018 The objective of this paper is to complete certain issues from our recent contribution (Calatayud, J.; Cortés, J.-C.; Jornet, M.; Villafuerte, L. Random non-autonomous second order linear differential equations: mean square analytic solutions and their ...
Julia Calatayud Gregori +2 more
doaj +1 more source
Computing derivative-based global sensitivity measures using polynomial chaos expansions [PDF]
, 2014 In the field of computer experiments sensitivity analysis aims at quantifying the relative importance of each input parameter (or combinations thereof) of a computational model with respect to the model output uncertainty.
B. Sudret +3 more
core
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf +2 more
wiley +1 more source
Distributional uncertainty analysis using polynomial chaos expansions [PDF]
2010 IEEE International Symposium on Computer-Aided Control System Design, 2010 A computationally efficient approach is presented that quantifies the influence of parameter uncertainties on the states and outputs of finite-time control trajectories for nonlinear systems, based on the approximate representation of the model via polynomial chaos expansion.
Zoltan K. Nagy, Richard D. Braatz
openaire +1 more source
1165. Echinacea purpurea (L.) Moench
Curtis's Botanical Magazine, EarlyView.Summary Echinacea purpurea (L.) Moench (Compositae: Heliantheae: Zinniinae) is described and illustrated with a colour plate and black and white text figure. An introduction to the history of the appearance of this species in the Magazine appears in brief, together with comments on the treatment of the genus, and the available generic revisions.
Nicholas Hind +2 more
wiley +1 more source
Polynomial-Chaos-based Kriging
, 2015 Computer simulation has become the standard tool in many engineering fields for designing and optimizing systems, as well as for assessing their reliability.
Schoebi, R., Sudret, B., Wiart, J.
core +1 more source
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms [PDF]
, 2022 Paul‐Christian Bürkner +3 more
openalex +1 more source
Stochastic Optimization using Polynomial Chaos Expansions
, 2020 Polynomial chaos based methods enable the efficient computation of output variability in the presence of input uncertainty in complex models. Consequently, they have been used extensively for propagating uncertainty through a wide variety of physical systems.
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The Journal of Physiology, EarlyView.Abstract figure legend Graphical representation of methods. We implemented three biventricular geometric models (Zenger et al., 2020) with rule‐based myocardial fibre orientations (Bayer et al., 2018). We evaluated variability in the fibre orientation via four sets of parameter distributions to determine the role of the primary and imbrication angles ...
Lindsay C. R. Tanner +8 more
wiley +1 more source
Deep Polynomial Chaos Expansion
Polynomial chaos expansion (PCE) is a classical and widely used surrogate modeling technique in physical simulation and uncertainty quantification. By taking a linear combination of a set of basis polynomials - orthonormal with respect to the distribution of uncertain input parameters - PCE enables tractable inference of key statistical quantities ...
Exenberger, Johannes +2 more
openaire +2 more sources