We develop a method to learn physical systems from data that employs feedforward neural
networks and whose predictions comply with the first and second principles of …

Discovery of dynamical systems from data forms the foundation for data-driven modeling
and recently, structure-preserving geometric perspectives have been shown to provide …

In this paper, a novel self-learning digital twin strategy is developed for fluid sloshing
phenomena. This class of problems is of utmost importance for robotic manipulation of fluids …

Thermodynamics-informed neural networks employ inductive biases for the enforcement of
the first and second principles of thermodynamics. To construct these biases, a metriplectic …

Structural dynamics systems are represented by discretized partial differential equations,
whose solutions depend on various parameters. Develo** high-fidelity numerical models …

Physics perception very often faces the problem that only limited data or partial
measurements on the scene are available. In this work, we propose a strategy to learn the …

For numerical simulation of elastic structures, data-driven computational approaches attempt
to use a data set of material responses, without resorting to conventional modeling of the …

This paper analyzes the ability of different machine learning techniques, able to operate in
the low-data limit, for constructing the model linking material and process parameters with …

Designing extrusion dies remains a tricky issue when considering polymers. In fact,
polymers exhibit strong non-Newtonian rheology that manifest in noticeable viscoelastic …

A nonparametric method assessing the error and variability margins in solutions depicted in
a separated form using experimental results is illustrated in this work. The method assess …