Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

Simulation-based engineering has been a major protagonist of the technology of the last
century. However, models based on well established physics fail sometimes to describe the …

The description of a physical problem through a model necessarily involves the introduction
of parameters. Hence, one wishes to have a solution of the problem that is a function of all …

In this paper, a multiparametric solution of the stiffness properties of woven composites
involving several microstructure parameters is performed. For this purpose, non-intrusive …

Many models assumed to be able to predict the response of structural systems fail to
efficiently accomplish that purpose because of two main reasons. First, some structures in …

Smart manufacturing implies creating virtual replicas of the processing operations, taking
into account the material dimension and its multi-physics transformation when forming …

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

To ensure correct filling in the resin transfer molding (RTM) process, adequate numerical
models have to be developed in order to correctly capture its physics, so that this model can …

Modeling systems from collected data faces two main difficulties: the first one concerns the
choice of measurable variables that will define the learnt model features, which should be …

Approximation of high-dimensional functions is a problem in many scientific fields that is
only feasible if advantageous structural properties, such as sparsity in a given basis, can be …