GREYDIENT: THE BEST OF TWO WORLDS

The mobility of the future needs safe and reliable intelligent systems. These should, among other things, enable self-driving vehicles to make the right decision at any time. In the GREYDIENT training network, ten international academic and industrial partners are working together to develop models that are up to the task. Prof David Moens of KU Leuven- De Nayer Campus is coordinating this pioneering project, hosting the project manager as well as two Early Stage Researchers of the project.

GREYDIENT is more than a research project,” Prof Moens clarifies. “We are primarily building an innovative network to train the next generation of researchers to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent systems.”

The research part of the project deals with the integration of data and mathematical models better known as 'black box' and 'white box'. Augustin Persoons, postdoctoral researcher at De Nayer Campus, explains the first type. “The black box is for example what we call machine learning or artificial intelligence. These “machines” can make predictions based only on data from past experiments. For example after traveling the same road a few times, a black box autonomous vehicle could be trained to repeat the same route. But that does not mean it is reliable in all circumstances. Any event that has not been experienced before such as a roadblock or an unforeseen rainstorm could completely confuse the vehicle considerably. Such data-driven system is called a black box. It can make fast predictions, but it is certainly not always accurate.”

Grey box

At the other end of the continuum is the white box modelling approach. “Here the laws of physics are programmed in advance,” explains PhD student Miriam Dodt. “The physics is properly simulated to provide accurate predictions. In the case of a vehicle, it could mean calculating optimal trajectories considering the law of vehicle dynamics, the weight of the car, the wind, the adhesion of tires and many more physical parameters. White box models are certainly accurate, but they need so much computer time before making a decision that the response may be too late. Whereas in such situations, it is just about reacting immediately.”

Prof Moens summarizes the distinction: “The white box is smart but slow; the black box is fast but not accurate. The challenge is to combine the advantages of both models in what is called a grey box. Such grey models are aimed at optimally integrating (black box) data-driven machine learning tools with (white box) simulation models to greatly surpass the performance of either framework separately.”

Prof. David Moens, Miriam Dodt, Damien Bonnet-Eymard, Anderson Vinha Pires, Augustin Persoons

© Joren De Weerdt

International

As part of the GREYDIENT training network, 15 young PhD students are being trained in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. “Thanks to this research, future mobility, factories and energy grids will become smarter, safer and more reliable,” said Prof Moens.

What further makes GREYDIENT unique is its pronounced international dimension. The 15 PhD students are expected to participate in eight one-week training events at different partner universities. A further two so-called secondments are planned in which the young researchers work abroad for a longer period of time. At least one of the secondments must be carried out in a company.

About these events and secondments, Prof Moens says: “The intention is that the researchers will build experience in communicating and disseminating their work, applying their research skills in a non-academic environment and receive in-depth training in transferable skills such as commercialisation, collaboration and entrepreneurship.”

Welding technology

One of the Phd students active at the De Nayer Campus is also collaborating with the Welding Engineering Group. This group focuses on resistance welding, arc welding, wire and arc additive manufacturing and process monitoring for welding. With Prof Moens' Reliable & Robust Design Group (R2D), a grey box is being developed for robust resistance pressure welding monitoring. The researcher is expected to build a digital twin to replace the black box.

Yves Persoons

www.greydient.eu