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    A Data Driven Black Box Approach for the Inverse Quantification of Set-Theoretical Uncertainty 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 011 ):;issue: 003:;page 31201-1
    Author(s): Bogaerts, Lars; Faes, Matthias G.R.; Moens, David
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Inverse uncertainty quantification commonly uses the well established Bayesian framework. Recently, alternative interval methodologies have been introduced. However, in their current state of the art implementation, both ...
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    Robust Design Optimization of Expensive Stochastic Simulators Under Lack-of-Knowledge 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2023:;volume( 009 ):;issue: 002:;page 21205-1
    Author(s): van Mierlo, Conradus; Persoons, Augustin; Faes, Matthias G. R.; Moens, David
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Robust design optimization of stochastic black-box functions is a challenging task in engineering practice. Crashworthiness optimization qualifies as such problem especially with regards to the high computational costs. ...
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    Special Section on Community Resilience to Disruptive Events: Models and Analyses, Lessons Learned and Case Studies 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2023:;volume( 009 ):;issue: 003:;page 30301-1
    Author(s): Wang, Cao; Faes, Matthias G. R.; Beer, Michael; Zio, Enrico; van de Lindt, John W.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The resilience of a community depends on the performance of the built environment (e.g., building portfolios and infrastructure systems) as well as the functionality of its constituting social and economic institutions. ...
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    Special Section: Nonprobabilistic and Hybrid Approaches for Uncertainty Quantification and Reliability Analysis 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2021:;volume( 007 ):;issue: 002:;page 020301-1
    Author(s): Faes, Matthias G. R.; Moens, David; Beer, Michael; Zhang, Hao; Phoon, Kok-Kwang
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: With this special section issue, we hope to illustrate in which direction the research of nonprobabilistic research is moving. From the included papers, it is clear that nonprobabilistic and hybrid methods are highly ...
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