POLYNOMIAL-CHAOS-BASED KRIGING

Classification Algorithms based on Generalized Polynomial Chaos

Time-Dependent Polynomial Chaos

Conventional generalized polynomial chaos is known to fail for long time integration, loosing its optimal convergence behaviour and developing unacceptable error levels. The reason for this loss of convergence is the assumption that the probability density function is constant in time. By allowing a probability density function to evolve in time the optimal properties of polynomial chaos are re...

polynomial chaos expansions KEVIN

Submitted for the MAR13 Meeting of The American Physical Society Simulation of stochastic quantum systems using polynomial chaos expansions KEVIN YOUNG, MATTHEW GRACE, Sandia National Laboratories — We present an approach to the simulation of quantum systems driven by classical stochastic processes that is based on the polynomial chaos expansion, a well-known technique in the field of uncertain...

SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos

A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistic...

Polynomial Chaos Based Design of Robust Input Shapers

A probabilistic approach, which exploits the domain and distribution of the uncertain model parameters, has been developed for the design of robust input shapers. Polynomial chaos expansions are used to approximate uncertain system states and cost functions in the stochastic space. Residual energy of the system is used as the cost function to design robust input shapers for precise rest-to-rest...