Managing computational complexity using surrogate models: a critical review
In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …
computational complexity. One critical issue that must be addressed is the approximation of …
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Microgrid digital twins: Concepts, applications, and future trends
Following the fourth industrial revolution, and with the recent advances in information and
communication technologies, the digital twinning concept is attracting the attention of both …
communication technologies, the digital twinning concept is attracting the attention of both …
Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …
essential if it is desired to simultaneously take advantage of both engineering principles and …
Modeling, analysis, and optimization under uncertainties: a review
Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …
has been an active area of research due to its evident advantages over deterministic design …
Quantification of model uncertainty: Calibration, model discrepancy, and identifiability
To use predictive models in engineering design of physical systems, one should first
quantify the model uncertainty via model updating techniques employing both simulation …
quantify the model uncertainty via model updating techniques employing both simulation …
[KIRJA][B] The science of risk analysis: Foundation and practice
T Aven - 2019 - taylorfrancis.com
This book provides a comprehensive demonstration of risk analysis as a distinct science
covering risk understanding, assessment, perception, communication, management …
covering risk understanding, assessment, perception, communication, management …
A tutorial on Bayesian inference to identify material parameters in solid mechanics
The aim of this contribution is to explain in a straightforward manner how Bayesian inference
can be used to identify material parameters of material models for solids. Bayesian …
can be used to identify material parameters of material models for solids. Bayesian …
A sequential calibration and validation framework for model uncertainty quantification and reduction
This paper aims to provide new insights into model calibration, which plays an essential role
in improving the validity of Modeling and Simulation (M&S) in engineering design and …
in improving the validity of Modeling and Simulation (M&S) in engineering design and …
Toward a better understanding of model validation metrics
Model validation metrics have been developed to provide a quantitative measure that
characterizes the agreement between predictions and observations. In engineering design …
characterizes the agreement between predictions and observations. In engineering design …