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 …
Review of statistical model calibration and validation—from the perspective of uncertainty structures
Computer-aided engineering (CAE) is now an essential instrument that aids in engineering
decision-making. Statistical model calibration and validation has recently drawn great …
decision-making. Statistical model calibration and validation has recently drawn great …
A model validation framework based on parameter calibration under aleatory and epistemic uncertainty
Abstract Model validation methods have been widely used in engineering design to
evaluate the accuracy and reliability of simulation models with uncertain inputs. Most of the …
evaluate the accuracy and reliability of simulation models with uncertain inputs. Most of the …
A Bayesian statistical method for quantifying model form uncertainty and two model combination methods
I Park, RV Grandhi - Reliability Engineering & System Safety, 2014 - Elsevier
Apart from parametric uncertainty, model form uncertainty as well as prediction error may be
involved in the analysis of engineering system. Model form uncertainty, inherently existing in …
involved in the analysis of engineering system. Model form uncertainty, inherently existing in …
Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments
The two-fluid model-based Multiphase Computational Fluid Dynamics (MCFD) solvers are
promising tools for a variety of engineering problems related to multiphase flows. Such a …
promising tools for a variety of engineering problems related to multiphase flows. Such a …
Integrating bayesian calibration, bias correction, and machine learning for the 2014 sandia verification and validation challenge problem
W Li, S Chen, Z Jiang… - Journal of …, 2016 - asmedigitalcollection.asme.org
This paper describes an integrated Bayesian calibration, bias correction, and machine
learning approach to the validation challenge problem posed at the Sandia Verification and …
learning approach to the validation challenge problem posed at the Sandia Verification and …
Validation metric based on Mahalanobis distance for models with multiple correlated responses
L Zhao, Z Lu, W Yun, W Wang - Reliability Engineering & System Safety, 2017 - Elsevier
In the probabilistic context, validation metric for models with multiple responses is essentially
used to measure the difference between joint statistical distributions resulting from …
used to measure the difference between joint statistical distributions resulting from …
仿真模型验证方法综述
李伟， 周玉臣， 林圣琳， 马萍， 杨明 - 系统仿真学报, 2019 - china-simulation.com
建模与仿真现已成为人们认识和研究客观世界的重要途径, 而仿真模型可信是开展仿真活动的
前提和基础. 随着仿真对象的复杂化和用户对仿真应用需求的提高, 仿真模型呈现出多种复杂 …
前提和基础. 随着仿真对象的复杂化和用户对仿真应用需求的提高, 仿真模型呈现出多种复杂 …
Nonhierarchical multi‐model fusion using spatial random processes
New model fusion techniques based on spatial‐random‐process modeling are developed in
this work for combining multi‐fidelity data from simulations and experiments. Existing works …
this work for combining multi‐fidelity data from simulations and experiments. Existing works …
Testing design optimization for uncertainty reduction in generating off-road mobility map using a Bayesian approach
Abstract The Next Generation NATO Reference Mobility Model (NG-NRMM) plays a vital role
in vehicle mobility prediction and mission planning. The complicated vehicle–terrain …
in vehicle mobility prediction and mission planning. The complicated vehicle–terrain …