Managing computational complexity using surrogate models: a critical review

R Alizadeh, JK Allen, F Mistree - Research in Engineering Design, 2020 - Springer
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 …

A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

A Thelen, X Zhang, O Fink, Y Lu, S Ghosh… - Structural and …, 2023 - Springer
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 …

Microgrid digital twins: Concepts, applications, and future trends

N Bazmohammadi, A Madary, JC Vasquez… - IEEE …, 2021 - ieeexplore.ieee.org
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 …

Perspectives on the integration between first-principles and data-driven modeling

W Bradley, J Kim, Z Kilwein, L Blakely… - Computers & Chemical …, 2022 - Elsevier
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 …

Modeling, analysis, and optimization under uncertainties: a review

E Acar, G Bayrak, Y Jung, I Lee, P Ramu… - Structural and …, 2021 - Springer
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 …

Quantification of model uncertainty: Calibration, model discrepancy, and identifiability

PD Arendt, DW Apley, W Chen - 2012 - asmedigitalcollection.asme.org
To use predictive models in engineering design of physical systems, one should first
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 …

A tutorial on Bayesian inference to identify material parameters in solid mechanics

H Rappel, LAA Beex, JS Hale, L Noels… - … Methods in Engineering, 2020 - Springer
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 …

A sequential calibration and validation framework for model uncertainty quantification and reduction

C Jiang, Z Hu, Y Liu, ZP Mourelatos, D Gorsich… - Computer Methods in …, 2020 - Elsevier
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 …

Toward a better understanding of model validation metrics

Y Liu, W Chen, P Arendt, HZ Huang - 2011 - asmedigitalcollection.asme.org
Model validation metrics have been developed to provide a quantitative measure that
characterizes the agreement between predictions and observations. In engineering design …