Conventional reduced order modeling techniques such as the reduced basis (RB) method
(relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …

We propose a data-driven Model Order Reduction (MOR) technique, based on Artificial
Neural Networks (ANNs), applicable to dynamical systems arising from Ordinary Differential …

For accurate and timely information on the evolving state of our volcanoes we need reliable
short-term forecasts. These forecasts directly impact crisis management from evacuations …

The development of data-informed predictive models for dynamical systems is of
widespread interest in many disciplines. We present a unifying framework for blending …

Abstract Model reduction methods aim to describe complex dynamic phenomena using only
relevant dynamical variables, decreasing computational cost, and potentially highlighting …

Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is
based on the introduction of two different surrogate models and an adaptive on-the-fly …

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial
differential equations (PDEs) that leverages models of different accuracy. From a collection …

This work proposes a machine-learning framework for modeling the error incurred by
approximate solutions to parameterized dynamical systems. In particular, we extend the …

Projection-based reduced-order models (pROMs) show great promise as a means to
accelerate many-query applications such as forward error propagation, solving inverse …