Engineering sciences and technology, as any other branch of sciences and technology, is experiencing the data revolution. In the past models were more abundant than data, too expensive to be collected and analyzed at that time. However, nowadays, the situation is radically different, data is much more abundant (and accurate) than existing models, and a new paradigm is emerging in engineering sciences and technology.

Advanced clustering techniques not only helps engineers and analysts, they become crucial in many areas where models, approximation bases, parameters, … are adapted depending on the local (in space and time senses) state of the system.

Machine learning is also helping for extracting the manifold in which the solutions of complex and coupled engineering problems are living. Thus, uncorrelated parameters can be efficiently extracted from the collected data coming from numerical simulations, experiments or even from the data collected from adequate measurement devices. As soon as uncorrelated parameters are identified (constituting the information level), the solution of the problem can be predicted in new points of the parametric space, from adequate interpolations (e.g. the nearest point on the manifold defined from the admissible solutions) or even more, parametric solutions can be obtained within an adequate framework able to circumvent the curse of dimensionality (combinatorial explosion) for any value of the uncorrelated model parameters.

Thus, the subtle circle is closed by linking data to information, information to knowledge and finally knowledge to real time decision-making, opening unimaginable possibilities within the so-called DDDAS (Dynamic Data Driven Application Systems).