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 …

This study investigates the accuracy of deep learning models for the inference of Reynolds-
averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net …

This paper examines the frame-invariance (and the lack thereof) exhibited in simulated
anisotropic elasto-plastic responses generated from supervised machine learning of …

An overview of the author's works, many of which were carried out in collaboration, is
presented. The first part concerns the quantification of uncertainties for complex engineering …

A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it
possible to synthesize solutions to a wide range of nonlinear stochastic boundary value …

The paper presents an appropriate and efficient methodology for updating the control
parameters of very large uncertain computational models, which are used for analyzing the …

This paper presents mathematical results in support of the methodology of the probabilistic
learning on manifolds (PLoM) recently introduced by the authors, which has been used with …

Die Bruchmechanik zählt zu den aufstrebenden und vielversprechendsten Gebieten der
Ingenieurmechanik. In den letzten Jahrzehnten wurden zahlreiche theoretische …

In a recent paper, the authors proposed a general methodology for probabilistic learning on
manifolds. The method was used to generate numerical samples that are statistically …

Purpose Deep-learning techniques are recently gaining a lot of importance in the field of
turbulence. This study focuses on addressing the problem of data imbalance to improve the …