Results 31 to 40 of about 1,546 (231)

Uncertainty Quantification in Small-Timescale Model-Based Fatigue Crack Growth Analysis Using a Stochastic Collocation Method

Metals, 2020
Due to the uncertainties originating from the underlying physical model, material properties and the measurement data in fatigue crack growth (FCG) processing, the prediction of fatigue crack growth lifetime is still challenging.
Hesheng Tang, Xueyuan Guo, Songtao Xue
doaj   +1 more source

Multi-fidelity stochastic collocation method for computation of statistical moments [PDF]

Journal of Computational Physics, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xueyu Zhu, Erin M. Linebarger, Dongbin Xiu   +2 more
openaire   +1 more source

The stochastic collocation Monte Carlo sampler: highly efficient sampling from ‘expensive’ distributions

, 2018
In this article, we propose an efficient approach for inverting computationally expensive cumulative distribution functions. A collocation method, called the Stochastic Collocation Monte Carlo sampler (SCMC sampler), within a polynomial chaos expansion ...
Witteveen, Jeroen, Grzelak, Lech Aleksander, Oosterlee, Kees, Suárez-Taboada, Maria   +3 more
core   +1 more source

Stochastic collocation for optimal control problems with stochastic PDE constraints

, 2022
S.2659-2682We discuss the use of stochastic collocation for the solution of optimal control problems which are constrained by stochastic partial differential equations (SPDE).
Kirby, R.M., Tiesler, H., Xiu, D.B., Preusser, T.   +3 more
core   +1 more source

Non‐Stationary Probabilistic Tsunami Hazard Assessments Compounding Tides and Sea Level Rise

Earth's Future, 2022
Tides are often the largest source of sea levels fluctuations. Two new probabilistic tsunami hazard assessments (PTHA) methods are proposed to combine the tidal phase uncertainty at the moment of tsunami occurrence with other sources of uncertainty.
Ignacio Sepúlveda, Philip L.‐F. Liu, Mircea Grigoriu, Jennifer S. Haase, Patricio Winckler   +4 more
doaj   +1 more source

A combined collocation and Monte Carlo method for advection-diffusion equation of a solute in random porous media

ESAIM: Proceedings and Surveys, 2014
In this work, we present a numerical analysis of a method which combines a deterministic and a probabilistic approaches to quantify the migration of a contaminant, under the presence of uncertainty on the permeability of the porous ...
Erhel Jocelyne, Mghazli Zoubida, Oumouni Mestapha   +2 more
doaj   +1 more source

Stochastic Optimization of Economic Dispatch With Wind and Photovoltaic Energy Using the Nested Sparse Grid-Based Stochastic Collocation Method

IEEE Access, 2019
Due to the increasing uncertainty brought about by renewable energy, conventional deterministic dispatch approaches have not been very applicative. This paper investigates a nested sparse grid-based stochastic collocation method (NS-SCM) as a possible ...
Zhilin Lu, Mingbo Liu, Wentian Lu, Zhuoming Deng   +3 more
doaj   +1 more source

Greedy nonlinear autoregression for multifidelity computer models at different scales

Energy and AI, 2020
Although the popular multi-fidelity surrogate models, stochastic collocation and nonlinear autoregression have been applied successfully to multiple benchmark problems in different areas of science and engineering, they have certain limitations.
W. Xing, M. Razi, R.M. Kirby, K. Sun, A.A. Shah   +4 more
doaj   +1 more source

A test of backward stochastic differential equations solver for solving semilinear parabolic differential equations in 1D and 2D

Partial Differential Equations in Applied Mathematics, 2022
Backward stochastic differential equation solver was first introduced by Han et al in 2017. A semilinear parabolic partial differential equation is converted into a stochastic differential equation, and then solved by the backward stochastic differential
Evan Davis, Guangming Yao, Elizabeth Javor, Kalani Rubasinghe, Luis Antonio Topete Galván   +4 more
doaj   +1 more source

Density estimation for time-dependent PDE with random input by a Legendre-based multi-element probabilistic collocation method

AIP Advances
This paper proposed a Legendre-based multi-element probabilistic collocation method for time-dependent stochastic differential equations, used for density estimation of unknown functions.
Hongling Xie
doaj   +1 more source