Development of a European collaborative proposal on big data and machine learning techniques for engineering numerical simulations

Project Description

The PEER activity aims at the development of a Horizon 2020 large-scale research proposal across the disciplines of computational mechanics and of data science. The planned proposal will address important aspects in the performance of engineering numerical simulations, especially those related to the extractions of information of engineering relevance from the ever-increasing amount of data developed by modern numerical simulations (e.g. in manufacturing or in infrastructure design and construction). In this aspect, the interdisciplinary collaboration with the field of data science and machine learning is a step change.
Two calls have been identified (ITN and RISE) as the most appropriate ones, with either or both being targeted according to the outcomes of the PEER activity. This activity will focus mainly on networking, developing collaborative relationships, project preparation, and development of proposals. This in turn will be enabled mainly through the organisation of two workshops with already identified academic and industrial partners, and also through other targeted networking activities (like individual visits) to engage with additional partners as soon as additional needs for the proposal are identified.
Besides the networking activities, project preparation and development of the proposal will also start within the activity, with input from all partners involved.

The proposal is led by Dr Stefanos Papanicolopulos at the University of Edinburgh