Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in
many fields and has been highly valued and strongly supported by academia and industry …

Uncertainty Quantification (UQ) is an essential step in computational model validation
because assessment of the model accuracy requires a concrete, quantifiable measure of …

This paper proposes an approach that combines reduced-order models with machine
learning in order to create physics-informed digital twins to predict high-dimensional output …

In nuclear reactor system design and safety analysis, the Best Estimate plus Uncertainty
(BEPU) methodology requires that computer model output uncertainties must be quantified …

This paper presents a Bayesian network model to assess the vulnerability of the flood
control infrastructure and to simulate failure cascade based on the topological structure of …

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-
scale framework was proposed in this paper by applying a physics-guided data-driven …

The continuous advancements in computer power and computational modeling through
high-fidelity and multiphysics simulations add more challenges on assessing their predictive …

Structural dynamics has numerous practical applications, such as structural analysis,
vibration control, energy harvesting, system identification, structural safety assessment, and …

Deep learning (DL) has achieved remarkable successes in many disciplines such as
computer vision and natural language processing due to the availability of “big data” …

This paper proposes a new uncertainty measure, appropriate for quantifying the
performance of transport models in assessing the origin or source of a given observation. It …