The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …

Uncertainty analysis in building energy assessment has become an active research field
because a number of factors influencing energy use in buildings are inherently uncertain …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Visual analytics supports humans in generating knowledge from large and often complex
datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the …

We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks,
including linear algebra, integration, optimization and solving differential equations, that …

Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …

Designing materials for targeted performance requirements as required in Integrated
Computational Materials Engineering (ICME) demands a combined strategy of bottom–up …

Surrogate-model-assisted uncertainty treatment practices have been the subject of
increasing attention and investigations in recent decades for many symmetrical engineering …

Due to its advantages of simplicity and high efficiency, Hasofer–Lind and Rackwitz–Fiessler
(HL–RF) method is widely used in structural reliability analysis. However, it may encounter …

A robust shape and topology optimization (RSTO) approach with consideration of random
field uncertainty in loading and material properties is developed in this work. The proposed …