Tag Archives: Engineering

Credibility is in the eye of the beholder

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Picture1Last month I described how computational models were used as more than fables in many areas of applied science, including engineering and precision medicine [‘Models as fables’ on March 16th, 2016].  When people need to make decisions with socioeconomic and, or personal costs, based on the predictions from these models, then the models need to be credible.  Credibility is like beauty, it is in the eye of the beholder.   It is a challenging problem to convince decision-makers, who are often not expert in the technology or modelling techniques, that the predictions are reliable and accurate.  After all, a model that is reliable and accurate but in which decision-makers have no confidence is almost useless.  In my research we are interested in the credibility of computational mechanics models that are used to optimise the design of load-bearing structures, whether it is the frame of a building, the wing of an aircraft or a hip prosthesis.  We have techniques that allow us to characterise maps of strain using feature vectors [see my post entitled ‘Recognising strain‘ on October 28th, 2015] and then to compare the ‘distances’ between the vectors representing the predictions and measurements.  If the predicted map of strain  is an perfect representation of the map measured in a physical prototype, then this ‘distance’ will be zero.  Of course, this never happens because there is noise in the measured data and our models are never perfect because they contain simplifying assumptions that make the modelling viable.  The difficult question is how much difference is acceptable between the predictions and measurements .  The public expect certainty with respect to the performance of an engineering structure whereas engineers know that there is always some uncertainty – we can reduce it but that costs money.  Money for more sophisticated models, for more computational resources to execute the models, and for more and better quality measurements.

Super channel system

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polina bayvelPerhaps we can be characterized by whether or not we believe we have an acceptable speed of internet access.  At home and work, I’m in the category that’s never satisfied by the speed provided.  Well, now there is a completely new standard: 1.125 Tb/s.  That’s 50,000 times faster than anything commercially available at the moment.  You could download a boxed set of the entire Games of Thrones saga in a second; at least that’s how Professor Polina Bayvel described her latest research in a recent conference that I attended at the Royal Society.  Professor Bayvel is head of the Optical Networks Group at University College London.  I think the UK government should abandon attempting to extend the current internet technology to everyone in the country and instead leap-frog the rest of the world by working on rolling out Prof Bayvel’s new technology.

Sources:

Maher R, Xu T, Galdino L, Sato M, Alvarado A, Shi K, Savory SJ, Thomsen BC, Killey RI & Bayvel P, Spectrally shaped DP-16QAM super-channel transmission with multi-channel digital back propagation, Scientific Reports, 5:8214, 2015.

Models as fables

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moel arthurIn his book, ‘Economic Rules – Why economics works, when it fails and how to tell the difference‘, Dani Rodrik describes models as fables – short stories that revolve around a few principal characters who live in an unnamed generic place and whose behaviour and interaction produce an outcome that serves as a lesson of sorts.  This seems to me to be a healthy perspective compared to the almost slavish belief in computational models that is common today in many quarters.  However, in engineering and increasingly in precision medicine, we use computational models as reliable and detailed predictors of the performance of specific systems.  Quantifying this reliability in a way that is useful to non-expert decision-makers is a current area of my research.  This work originated in aerospace engineering where it is possible, though expensive, to acquire comprehensive and information-rich data from experiments and then to validate models by comparing their predictions to measurements.  We have progressed to nuclear power engineering in which the extreme conditions and time-scales lead to sparse or incomplete data that make it more challenging to assess the reliability of computational models.  Now, we are just starting to consider models in computational biology where the inherent variability of biological data and our inability to control the real world present even bigger challenges to establishing model reliability.

Sources:

Dani Rodrik, Economic Rules: Why economics works, when it fails and how to tell the difference, Oxford University Press, 2015

Patterson, E.A., Taylor, R.J. & Bankhead, M., A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016

Hack, E., Lampeas, G. & Patterson, E.A., An evaluation of a protocol for the validation of computational solid mechanics models, J. Strain Analysis, 51(1):5-13, 2016.

Patterson, E.A., Challenges in experimental strain analysis: interfaces and temperature extremes, J. Strain Analysis, 50(5): 282-3, 2015

Patterson, E.A., On the credibility of engineering models and meta-models, J. Strain Analysis, 50(4):218-220, 2015

A liberal engineering education

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115-1547_IMGFredrik Sjoberg points out how the lives of Darwin and Linnaeus have become models for generations of natural scientists.  Youthful travels followed by years of patient, narrowly focussed research and finally the revolutionary ideas and great books.  Very many scientists have followed the first two steps but missed out on the last one, leaving them trapped in ‘the tunnel vision of specialised research’.  As our society and its accompanying technology has become more complex, more and more tunnels or silos of specialised knowledge and research have been created.  This has led specialists to focus on solving issues that they understand best and communicating little or not at all with others in related fields.  At the same time, our society and technologies are becoming more interconnected, making it more appropriate to cross the cultural divides between specialisms.

One of the pleasures of teaching my current MOOC is the diversity of learners in terms of gender, geography and educational background who are willing to cross the cultural divides.  We have people following the MOOC in places as diverse as Iceland, Mexico, Nigeria and Syria.  We have coffee bean growers, retired humanities academics, physical chemists and social historians.  In most of the western world, engineering is taught to male-dominated classes and this has remained a stubborn constant despite strenous efforts to bring about change.  So it is a pleasure to interact with such a diverse cohort of people seeking to liberate their minds from habit and convention.

The original meaning of the term ‘liberal studies’ was studies that liberated students’ minds from habit and convention.  Recently, Vinod Khosla has suggested that we should focus on teaching our students ‘liberal sciences’.     This seems to connect with the ’emotive traits’ that David Brooks has proposed will be required for success in the future, when machines can do most of what humans do now (see my post entitled ‘Smart Machines‘ on February 26th, 2014).  These emotive traits are a voracious lust of understanding, an enthusiasm for work, the ability to grasp the gist and an empathetic sensitivity for what will attract attention.   We don’t teach much of any of these in traditional engineering degrees which is perhaps why we can’t recruit a more diverse student population.  We need to incorporate them into our degree programmes, reduce much of the esoteric brain-twisting analysis and encourage our students to grapple with concepts and their broader implications.  This would become a liberal engineering education.

Sources:

Fredrik Sjoberg, The Fly Trap, Penguin Books, 2015

Asish Ghosh, Dynamic Systems for Everyone: Understanding How Our World Works, Springer, 2015

Vinod Khosla, Is majoring in liberal arts a mistake for students? Medium, February 10th, 2016

David Brooks, What machines can’t do, New York Times, February 3rd, 2014