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 …
Imprecise probabilities in engineering analyses
Probabilistic uncertainty and imprecision in structural parameters and in environmental
conditions and loads are challenging phenomena in engineering analyses. They require …
conditions and loads are challenging phenomena in engineering analyses. They require …
Key computational modeling issues in integrated computational materials engineering
Designing materials for targeted performance requirements as required in Integrated
Computational Materials Engineering (ICME) demands a combined strategy of bottom–up …
Computational Materials Engineering (ICME) demands a combined strategy of bottom–up …
Ensemble machine learning models for aviation incident risk prediction
X Zhang, S Mahadevan - Decision Support Systems, 2019 - Elsevier
With the spectacular growth of air traffic demand expected over the next two decades, the
safety of the air transportation system is of increasing concern. In this paper, we facilitate the …
safety of the air transportation system is of increasing concern. In this paper, we facilitate the …
Generalized evidence theory
Y Deng - Applied Intelligence, 2015 - Springer
Dempster-Shafer evidence theory is an efficient tool in knowledge reasoning and decision-
making under uncertain environments. Conflict management is an open issue in Dempster …
making under uncertain environments. Conflict management is an open issue in Dempster …
[图书][B] Mastering uncertainty in mechanical engineering
PF Pelz, P Groche, ME Pfetsch, M Schaeffner - 2021 - library.oapen.org
This open access book reports on innovative methods, technologies and strategies for
mastering uncertainty in technical systems. Despite the fact that current research on …
mastering uncertainty in technical systems. Despite the fact that current research on …
Quantitative model validation techniques: New insights
Y Ling, S Mahadevan - Reliability Engineering & System Safety, 2013 - Elsevier
This paper develops new insights into quantitative methods for the validation of
computational model prediction. Four types of methods are investigated, namely classical …
computational model prediction. Four types of methods are investigated, namely classical …
Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems
S Sankararaman, S Mahadevan - Reliability Engineering & System Safety, 2015 - Elsevier
This paper proposes a Bayesian methodology to integrate model verification, validation, and
calibration activities for the purpose of overall uncertainty quantification in different types of …
calibration activities for the purpose of overall uncertainty quantification in different types of …
Stochastic model updating utilizing Bayesian approach and Gaussian process model
HP Wan, WX Ren - Mechanical Systems and Signal Processing, 2016 - Elsevier
Stochastic model updating (SMU) has been increasingly applied in quantifying structural
parameter uncertainty from responses variability. SMU for parameter uncertainty …
parameter uncertainty from responses variability. SMU for parameter uncertainty …
Deconvolution of electrochemical impedance spectroscopy data using the deep-neural-network-enhanced distribution of relaxation times
Electrochemical impedance spectroscopy (EIS) is widely used to characterize
electrochemical systems. The distribution of relaxation times (DRT) has emerged as a …
electrochemical systems. The distribution of relaxation times (DRT) has emerged as a …