Sparse polynomial chaos expansions: Literature survey and benchmark
Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
Machine learning for cardiovascular biomechanics modeling: challenges and beyond
Recent progress in machine learning (ML), together with advanced computational power,
have provided new research opportunities in cardiovascular modeling. While classifying …
have provided new research opportunities in cardiovascular modeling. While classifying …
[图书][B] Active subspaces: Emerging ideas for dimension reduction in parameter studies
PG Constantine - 2015 - SIAM
Parameter studies are everywhere in computational science. Complex engineering
simulations must run several times with different inputs to effectively study the relationships …
simulations must run several times with different inputs to effectively study the relationships …
[图书][B] Uncertainty quantification
C Soize - 2017 - Springer
This book results from a course developed by the author and reflects both his own and
collaborative research regarding the development and implementation of uncertainty …
collaborative research regarding the development and implementation of uncertainty …
Polynomial-chaos-based Kriging
Computer simulation has become the standard tool in many engineering fields for designing
and optimizing systems, as well as for assessing their reliability. Optimization and …
and optimizing systems, as well as for assessing their reliability. Optimization and …
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis
The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit
provides a exible and extensible interface between simulation codes and iterative analysis …
provides a exible and extensible interface between simulation codes and iterative analysis …
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: version 6.13 …
The Dakota toolkit provides a flexible and extensible interface between simulation codes
and iterative analysis methods. Dakota contains algorithms for optimization with gradient …
and iterative analysis methods. Dakota contains algorithms for optimization with gradient …
A new surrogate modeling technique combining Kriging and polynomial chaos expansions–Application to uncertainty analysis in computational dosimetry
In numerical dosimetry, the recent advances in high performance computing led to a strong
reduction of the required computational time to assess the specific absorption rate (SAR) …
reduction of the required computational time to assess the specific absorption rate (SAR) …
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes
Global sensitivity analysis is now established as a powerful approach for determining the
key random input parameters that drive the uncertainty of model output predictions. Yet the …
key random input parameters that drive the uncertainty of model output predictions. Yet the …
Stochastic finite element methods for partial differential equations with random input data
MD Gunzburger, CG Webster, G Zhang - Acta Numerica, 2014 - cambridge.org
The quantification of probabilistic uncertainties in the outputs of physical, biological, and
social systems governed by partial differential equations with random inputs require, in …
social systems governed by partial differential equations with random inputs require, in …