The widespread incidence of cardiovascular diseases and associated mortality and
morbidity, along with the advent of powerful computational resources, have fostered an …

This paper presents a novel non-intrusive surrogate modeling scheme based on deep
learning for predictive modeling of complex systems, described by parametrized time …

This paper provides a coherent and efficient computational framework for stochastic
multiscale analysis of material systems in the presence of parametric uncertainties and …

All kinds of uncertainties influence the reliability of the engineering system. Thus, uncertainty
quantification is significant to the system reliability analysis. Polynomial chaos expansion …

In-stent restenosis is a recurrence of coronary artery narrowing due to vascular injury
caused by balloon dilation and stent placement. It may lead to the relapse of angina …

Background Restenosis following percutaneous transluminal angioplasty (PTA) in femoral
arteries is a major cause of failure of the revascularization procedure. The arterial wall …

Various real world problems deal with data-driven uncertainty. In particular, in geophysical
applications the amount of available data is often limited, posing a challenge in the …

We present the VECMA toolkit (VECMAtk), a flexible software environment for single and
multiscale simulations that introduces directly applicable and reusable procedures for …

Temperature field reconstruction is essential for analyzing the reliability of a high-density
functionally integrated satellite circuit board (HFI-SCB). As a representative deep learning …

Uncertainty quantification (UQ) is a key component when using computational models that
involve uncertainties, eg in decision-making scenarios. In this work, we present uncertainty …