Results 41 to 50 of about 7,268 (236)
Deep Polynomial Chaos Expansion
CoRR29th International Conference on Artificial Intelligence and Statistics (AISTATS ...
Johannes Exenberger +2 more
openaire +2 more sources
Extending Polynomial Chaos to Include Interval Analysis [PDF]
IEEE Transactions on Instrumentation and Measurement, 2008 Polynomial chaos theory (PCT) has been proven to be an efficient and effective way to represent and propagate uncertainty through system models and algorithms in general. In particular, PCT is a computationally efficient way to analyze and solve dynamic models under uncertainty. This paper presents a new way to use a polynomial expansion to incorporate
Antonello Monti +2 more
openaire +1 more source
Recent advances in polynomial chaos method for uncertainty propagation
Zhongguo Jianchuan Yanjiu, 2021 Uncertainty exists widely in engineering design. As one of the key components of engineering design, uncertainty propagation and quantification has always been an important research topic.
Fenfen XIONG +4 more
doaj +1 more source
Identification of ground anchors reliability based on acceptance tests and the polynomial chaos expansion method [PDF]
Archives of Civil EngineeringThe paper presents a reliability analysis of ground anchors based on acceptance tests and the polynomial chaos expansion method. First of all, it was estimated the probability of meeting the requirements of acceptance tests based on anchor tests realised
Marek Wyjadłowski +3 more
doaj +1 more source
Decoupling Inequalities for Polynomial Chaos
The Annals of Probability, 1987 Let \(X,X_ 1,...,X_ d\) be a sequence of independent, symmetric, identically distributed random vectors with independent components. The main subject of this paper is the so-called decoupling inequalities, i.e., inequalities of the form \[ E\phi (cQ(X,X,...,X))\leq E\phi (Q(X_ 1,X_ 2,...,X_ d))\leq E\phi (CQ(X,X,...,X)), \] where Q is a symmetric ...
openaire +2 more sources
On the convergence of generalized polynomial chaos expansions [PDF]
, 2011 A number of approaches for discretizing partial differential equations with random data are based on generalized polynomial chaos expansions of random variables.
Starkloff, Hans-Jörg +7 more
core +1 more source
Yield-Constrained Optimization Design Using Polynomial Chaos for Microwave Filters
IEEE Access, 2021 Yield optimization aims at finding microwave filter designs with high yield under fabrication tolerance. The electromagnetic (EM) simulation-based yield optimization methods are computationally expensive because a large number of EM simulations is ...
Zhen Zhang +4 more
doaj +1 more source
Synchronization of Analog Neuron Circuits With Digital Memristive Synapses: An Hybrid Approach
Advanced Electronic Materials, EarlyView.An hybrid circuit mimicking neural units coupled using memristive synapses is introduced. The analog neurons provide flexibility and robustness, and the digital memristive coupling guarantees the full reconfigurability of the interconnection. The onset of a synchronized spiking behavior in two circuits mimicking the Izhikevich neuron is discussed from ...
Lamberto Carnazza +3 more
wiley +1 more source
Determination of Bootstrap confidence intervals on sensitivity indices obtained by polynomial chaos expansion [PDF]
, 2012 L’analyse de sensibilité a pour but d’évaluer l’influence de la variabilité d’un ou plusieurs paramètres d’entrée d’un modèle sur la variabilité d’une ou plusieurs réponses.
Salaün, Michel +3 more
core
Polynomial chaos expansion for operator learning
Computer Methods in Applied Mechanics and EngineeringOperator learning (OL) has emerged as a powerful tool in scientific machine learning (SciML) for approximating mappings between infinite-dimensional functional spaces. One of its main applications is learning the solution operator of partial differential equations (PDEs).
Himanshu Sharma +2 more
openaire +2 more sources