The BUQEYE collaboration (Bayesian Uncertainty Quantification: Errors in Your effective
field theory) presents a pedagogical introduction to projection-based, reduced-order …

The digital twin concept represents an appealing opportunity to advance condition-based
and predictive maintenance paradigms for civil engineering systems, thus allowing reduced …

The field of model order reduction (MOR) is growing in importance due to its ability to extract
the key insights from complex simulations while discarding computationally burdensome …

In order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake characteristics, in this
paper we present high-fidelity CFD simulations of the flow around an exemplary H-shaped …

In the present work, we introduce a novel approach to enhance the precision of reduced
order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models …

The simulation of atmospheric flows by means of traditional discretization methods remains
computationally intensive, hindering the achievement of high forecasting accuracy in short …

Geometrically parametrized partial differential equations are currently widely used in many
different fields, such as shape optimization processes or patient-specific surgery studies …

We propose a local, non-intrusive model order reduction technique to accurately
approximate the solution of coupled multi-component parametrized systems governed by …

The investigation of fluid-solid systems is very important in a lot of industrial processes. From
a computational point of view, the simulation of such systems is very expensive, especially …

This work introduces a novel approach for data-driven model reduction of time-dependent
parametric partial differential equations. Using a multi-step procedure consisting of proper …