ECOLE — Experience-based Computation: Learning to Optimise

Results in brief

Experience-based Computation: Learning to Optimise

Europe is facing challenges for growth of technological companies as the increasing complexity of products, development and production processes requires advanced integration skills and innovative minds. There is an acute shortage of human experts with sufficient skills in tackling current industrial challenges in a holistic manner, and meanwhile there is a need for solving ever more complex problems to tackle such challenges. The aim of the ECOLE ITN is to contribute towards shortening product cycles, reducing resource consumption during complete engineering processes and creating more balanced and innovative products. This will involve not only the development of novel approaches to solving complex engineering optimisation problems, but also the equipment of early career researchers with the skills needed to advance European technological companies in the years to come, enhancing their employability and strengthening European innovation capacity. In particular, the ECOLE project will train early career researchers with strong skills in artificial intelligence and optimisation, which are in high demand by European industries. ECOLE’s aim has been achieved through meeting the following core objectives: (1) The development of a novel “Learning to Optimise” framework. ECOLE has taken a bold step forward in solving complex engineering optimisation problems. Instead of just developing technologies to solve a given optimisation problem instance, it has proposed novel techniques to optimise automatically across problem instances. Through knowledge, skills, and practices derived from problem solving processes in time, the experience of optimising one product or process can be learned and transferred automatically to better solve other complex optimisation problems. (2) The development of early-stage researchers’ (ESRs’) transferrable skills and industrial experiences. Among others, this has included technical knowledge that enables them to understand the interplay between learning and optimisation, practical skills in integrating various disciplines into innovative solutions to complex industrial problems, competences related to intellectual property, management and entrepreneurship, and communication skills.

Data: CORDIS, © European Union

Project objective

The overall theme of our proposed doctoral programme is ECOLE: Experience-based COmputation: Learning to optimisE. It seeks novel synergies between nature inspired optimisation and machine learning to address new challenges that arise in industry due to the increasing complexity of products, product development and production processes. The unique aspect of ECOLE is to study and capture the notion of experience that is associated with expert engineers, who have worked on complex optimisation tasks for a certain time, in a computational framework composed of machine learning and optimisation strategies. We aim at developing cutting-edge optimisation algorithms that can continuously accumulate experience by learning from development projects both over time and across different problem categories. The more such algorithms are used for different optimisation problems, the better they become since their accumulated experience increases. The Consortium consists of two world-leading universities, the University of Birmingham (UK) and the University of Leiden (The Netherlands), both in the top 150 in the 2016-17 Times Higher Education World University Rankings, and two innovative companies, Honda Research Institute Europe GmbH (Germany) in the automotive sector and NEC Europe Ltd (UK) in the ICT sector. All have world-leading research groups with complementary expertise that support ECOLE. ECOLE fills an urgent need in Europe for highly skilled optimisation and machine learning experts who have first-hand industrial experiences allowing sustainable know-how growth for solving future challenges. Its entire training programme is centred around a set of novel research projects proposed for early stage researchers (ESRs), complemented by domain knowledge training, hands-on engineering training and transferable skill training. ESRs will spend 50% of their time in the non-academic beneficiaries and be trained in different academic environments and industrial sectors.

Original text from CORDIS.

Participants

THE UNIVERSITY OF BIRMINGHAM · BirminghamCoordinatorUnited Kingdom
BAYERISCHE MOTOREN WERKE AKTIENGESELLSCHAFT · MUNCHENGermany
DYNAMORE HOLDING GMBH · STUTTGART VAIHINGENGermany
Honda Research Institute Europe GmbH · Offenbach/MainGermany
NEC EUROPE LTD · LondonUnited Kingdom
NEC LABORATORIES EUROPE GMBH · HeidelbergGermany
Tata Steel Nederland bv · IjmuidenNetherlands
UNIVERSITEIT LEIDEN · LeidenNetherlands

ECOLE · Experience-based Computation: Learning to Optimise

Results in brief

Experience-based Computation: Learning to Optimise

Project objective

Participants

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