Results 181 to 190 of about 1,013 (219)
Some of the next articles are maybe not open access.
Probabilistic Load Flow Based on Generalized Polynomial Chaos
IEEE Transactions on Power Systems, 2017An analytical method based on generalized polynomial chaos (gPC) is proposed for probabilistic load flow (PLF). The method preserves the nonlinearity of power flow equations whose rectangular formulations are adopted to facilitate the gPC expansion. The feasibility of the method is demonstrated by case studies from a 9-bus system.
Hao Wu +3 more
openaire +1 more source
Aircraft Safety Analysis Using Generalized Polynomial Chaos
2019In this paper we investigate the application of generalized polynomial chaos (gPC) for optimal control based aircraft safety assessment with parameter uncertainties. The approach is based on the formulation of an appropriate optimal control problem to obtain worst case inputs.
J. Diepolder +4 more
openaire +1 more source
Constructing Continuous-Time Chaos-Generating Templates Using Polynomial Approximation
ECMS 2010 Proceedings edited by A Bargiela S A Ali D Crowley E J H Kerckhoffs, 2010Aiming at developing a methodology for constructing continuous-time chaotic dynamical systems as flexible pattern generators, this paper discusses a strategy for binding desired unstable periodic orbits into a chaotic attractor. The strategy is comprised of the following two stages: constructing an interim “chaos-generating template”, and deforming the
Hidetaka Ito +3 more
openaire +1 more source
Generalized Polynomial Chaos-based Ensemble Kalman Filtering for Orbit Estimation
2021 American Control Conference (ACC), 2021In this paper a novel framework is proposed to carry out state and parameter estimation of a general nonlinear stochastic dynamical system in a prediction-correction fashion. The uncertainties in the initial states and parameters are propagated using generalized polynomial chaos expansion technique to compute the predicted estimates of states and ...
Rajnish Bhusal, Kamesh Subbarao
openaire +1 more source
General Introduction to Polynomial Chaos and Collocation Methods
2018In this chapter, the basic principles of two methodologies for uncertainty quantification (UQ) are discussed, namely the polynomial chaos method and the collocation method. UQ deals with the propagation of uncertainties through complex numerical models, and in the present context of the UMRIDA project, mostly computational fluid dynamics (CFD) codes ...
Chris Lacor, Éric Savin
openaire +1 more source
Generalized polynomial chaos-based estimation of human knee stiffness
2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2016Observation of the human knee stiffness is known to be an important issue in rehabilitation robotics in order to consider biomechanical knee parameters of the individual patient. As in-vivo identification often is a complicated task, modeling of biomechanical processes always comes with uncertainties.
Markus Lueken +2 more
openaire +1 more source
Generalized polynomial chaos for nonlinear random delay differential equations
Applied Numerical Mathematics, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shi, Wenjie, Zhang, Chengjian
openaire +1 more source
Generalized polynomial chaos based surrogate models for acoustics and vibrations
The Journal of the Acoustical Society of America, 2022This work explores generalized polynomial chaos (GPC) surrogate models and their effectiveness for general acoustics and vibration applications. GPC is primarily known as an uncertainty quantification (UQ) technique and in that context the underlying polynomial-based model has been shown to be effective in mapping input probability distributions to the
openaire +1 more source
A Goal-Oriented Reduced Basis Methods-Accelerated Generalized Polynomial Chaos Algorithm
SIAM/ASA Journal on Uncertainty Quantification, 2016Summary: The nonintrusive generalized polynomial chaos (gPC) method is a popular computational approach for solving partial differential equations with random inputs. The main hurdle preventing its efficient direct application for high-dimensional input parameters is that the size of many parametric sampling meshes grows exponentially in the number of ...
Jiang, Jiahua +2 more
openaire +1 more source
Cardiac image segmentation using generalized polynomial chaos expansion and level set function
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017Cardiovascular Magnetic Resonance (CMR) images involves a great amount of uncertainties. Such uncertainties may originate from either intrinsic measurement limitations or heterogeneities among patients. Without properly considering these uncertainties, image analysis may provide inaccurate estimations of cardiac functions, and ultimately lead to false ...
, Yuncheng Du, , Dongping Du
openaire +2 more sources