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Department of Engineering

Data Centric Engineering International Workshop

Data Centric Engineering International Workshop

World-leading researchers and practitioners in all aspects of Data Centric Engineering will convene at an International workshop from 9-12 December 2019

Data Centric Engineering International workshop 9-12 December 2019

The past decade has seen an enormous growth in the technological infrastructure and the mathematical/statistical underpinnings of data science. Our ability to collect, store, process, and interpret data has advanced immeasurably, and data-driven methods have had unprecedented impact on business and society at large, in large part because of their success in analysing and predicting human preferences. Engineering stands to benefit from this data revolution in similar ways, but realising this vision requires thoughtful and concerted effort.

The Lloyd’s Register Foundation Foresight Review of Big Data calls for putting “data considerations at the core of engineering design. . . [to improve] the performance, safety, reliability and efficiency of assets, infrastructures and complex machines” with data analytics featuring “at all phases of the life-cycle of engineered systems.” Key examples include critical infrastructure networks (transportation, water distribution, power grids, and farm ecosystems) where resilience and real-time adaptability are essential; health monitoring and predictive maintenance of complex assets, ranging from jet engines to the built environment, where data-driven decisions can improve safety and reduce costs; and the engineering design process itself, where data-driven modelling can accelerate the design cycle while yielding more capable and predictable products

Lord Robert MairThe aim of the workshop is not only to convene world-leading researchers and practitioners in all aspects of Data Centric Engineering, but also to produce a community-led manifesto defining this new field. The workshop will build on the successful Data-Centric Engineering program headquartered at the Alan Turing Institute in London, and will serve to “spread the word” of Data Centric Engineering beyond the British scientific ecosystem, via MIT, to the USA and beyond.

Lord Robert Mair (pictured left) will be speaking at the event.

Data Centric Engineering seeks to span—and indeed link—fundamental theoretical foundations with direct impact on application domains within all sectors of engineering enterprise. The workshop will thus feature “pull” from key engineering disciplines as well as “push” from engineering science, mathematicians, statisticians, and computer scientists, and management scientists. In keeping with the Data Centric Engineering ethos, the engineering disciplines will envision and define broad data-centric challenges over the next five, ten, and thirty years. In response, researchers from the foundational research communities will distil these challenges into conceptual and methodological needs and gaps, and to envision strategies for addressing these gaps in support of the engineering applications.​

Relevant engineering disciplines include mechanical engineering, civil, geotechnical and environmental engineering, aeronautical and astronautical engineering, chemical engineering, petroleum engineering, nuclear engineering, biomedical engineering, materials science, transportation, and autonomous systems. Examples of multidisciplinary application areas that will be integrated into the workshop are:

  • Asset  performance optimisation such as predictive maintenance and health monitoring;

  • Synthesising data into models based on fundamental laws – so called digital twins;

  • data-driven materials discovery and design;

  • risk assessment using data and physical models;

  • robust and resilient data-driven engineering design.​

These application areas will be integrated with their relevant methodological thrusts, that include: 

  • inverse problems and data assimilation;

  • computational algorithms for uncertainty quantification and design under uncertainty;

  • optimal experimental design, optimal sensing and learning, data reduction;

  • data-driven modelling and closure;

  • systematic integration of data with physical principles or constraints;

  • data-driven dimension reduction;

  • systems perspectives on operations.​

The workshop will be single-track, to promote interdisciplinary exchange and discussion. The combination of talks focused on engineering disciplines and methodology should realise the intended “pull” from the engineering challenges and “push” from the theory, methods, and algorithms. There will also be a series of panel discussions following each disciplinary session, which will have the following aims:

  • Cover key data-centric engineering challenges from the collection and storage of data, to its processing, and extracting value—i.e. engineering insight.

  • Enumerate the challenges underpinning the methodological thrusts and identify open problems.

  • Communicate the impact of data-centric engineering and inform the wider engineering and data science community of the opportunities.​

In addition to these disciplinary panel discussions there will also be one focused on education, and one on funding, with the following objectives:

  • Develop a consensus on how undergraduate engineering curricula can be vitalised with a more data-centric engineering focus, e.g. instead of having classes on uncertainty quantification and inverse problems, should we design courses such as uncertainty assessments and quantification in turbomachinery and inverse problems in civil structures?

  • Provide clarity on the role that professional bodies (e.g. Institute of Physics, Royal Academy of Engineering, American Society of Mechanical Engineers, etc.) need to play in promoting data-centric engineering training for engineering professionals.

  • Scope out new initiatives that funding agencies, e.g. UK Research and Innovation (UKRI), Engineering and Physical Sciences Research Council (EPSRC), European Research Council (ERC), and the US National Science Foundation (NSF), could adopt to foster greater data-centric engineering research efforts, and discuss how academics can facilitate greater cross disciplinary synergies with such grants.

During the workshop, there will be daily open time allocated for participants to engage in independent discussions. The final day of the workshop will be devoted to a structured discussion, aimed at linking the outcomes of the three previous days’ sessions—identifying common threads and chartering a vision on research, education and funding.


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