Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …

This paper introduces a new approach for importance-sampling-based reliability-based
design optimization (RBDO) that reuses information from past optimization iterations to …

This work introduces a new method to efficiently solve optimization problems constrained by
partial differential equations (PDEs) with uncertain coefficients. The method leverages two …

We present a surrogate modeling framework for conservatively estimating measures of risk
from limited realizations of an expensive physical experiment or computational simulation …

Reliable, risk-averse design of complex engineering systems with optimized performance
requires dealing with uncertainties. A conventional approach is to add safety margins to a …

The prevalence of uncertainty in models of engineering and the natural sciences
necessitates the inclusion of random parameters in the underlying partial differential …

Deep Learning is having a remarkable impact on the design of Reduced Order Models
(ROMs) for Partial Differential Equations (PDEs), where it is exploited as a powerful tool for …

Multifidelity methods leverage low-cost surrogate models to speed up computations and
make occasional recourse to expensive high-fidelity models to establish accuracy …

This paper presents a new computational method for forward uncertainty quantification (UQ)
analyses on large-scale structural systems in the presence of arbitrary and dependent …

In optimal control problems, often initial data are required that are not known exactly in
practice. In order to take into account this uncertainty, we consider optimal control problems …