Tag Archives: Einstein

In Einstein’s footprints?

Grand Hall of the Guild of Carpenters, Zurich

During the past week, I have been working with members of my research group on a series of papers for a conference in the USA that a small group of us will be attending in the summer.  Dissemination is an important step in the research process; there is no point in doing the research if we lock the results away in a desk drawer and forget about them.  Nowadays, the funding organisations that support our research expect to see a plan of dissemination as part of our proposals for research; and hence, we have an obligation to present our results to the scientific community as well as to communicate them more widely, for instance through this blog.

That’s all fine; but nevertheless, I don’t find most conferences a worthwhile experience.  Often, there are too many uncoordinated sessions running in parallel that contain presentations describing tiny steps forward in knowledge and understanding which fail to compel your attention [see ‘Compelling presentations‘ on March 21st, 2018].  Of course, they can provide an opportunity to network, especially for those researchers in the early stages of their careers; but, in my experience, they are rarely the location for serious intellectual discussion or debate.  This is more likely to happen in small workshops focussed on a ‘hot-topic’ and with a carefully selected eclectic mix of speakers interspersed with chaired discussion sessions.

I have been involved in organising a number of such workshops in Glasgow, London, Munich and Shanghai over the last decade.  The next one will be in Zurich in November 2019 in Guild Hall of Carpenters (Zunfthaus zur Zimmerleuten) where Einstein lectured in November 1910 to the Zurich Physical Society ‘On Boltzmann’s principle and some of its direct consequences‘.  Our subject will be different: ‘Validation of Computational Mechanics Models’; but we hope that the debate on credible models, multi-physics simulations and surviving with experimental data will be as lively as in 1910.  If you would like to contribute then download the pdf from this link; and if you just like to attend the one-day workshop then we will be announcing registration soon and there is no charge!

We have published the outcomes from some of our previous workshops:

Advances in Validation of Computational Mechanics Models (from the 2014 workshop in Munich), Journal of Strain Analysis, vol. 51, no.1, 2016

Strain Measurement in Extreme Environments (from the 2012 workshop in Glasgow), Journal of Strain Analysis, vol. 49, no. 4, 2014.

Validation of Computational Solid Mechanics Models (from the 2011 workshop in Shanghai), Journal of Strain Analysis, vol. 48, no.1, 2013.

The workshop is supported by the MOTIVATE project and further details are available at http://www.engineeringvalidation.org/4th-workshop

The MOTIVATE project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 754660.

Blind to complexity

fruit fly nervous system Albert Cardona HHMI Janelia Research Campus Welcome Image Awards 2015When faced with complexity, we tend to seek order and simplicity.  Most of us respond negatively to the uncertainty associated with complex systems and their apparent unpredictability.  Complex systems can be characterised as large networks operating using simple rules but without central control which results in self-organising behaviour and non-trivial emergent behaviour.  Emergent behaviour is the behaviour of the system that is not apparent or expected from the behaviour of its constituent parts [see ‘Emergent properties‘ on September 16th, 2015].

The philosopher, William Wimsatt observed that we tend to ignore phenomena whose complexity exceeds our predictive capability and our detection apparatus.  This is problematic because we try to over-simplify our descriptions of complex systems.  Occam’s razor is often mis-interpreted to mean that simple explanations are better ones, whereas in reality ‘everything should be made as simple as possible, but not simpler’, (which is often attributed to Einstein).  This implies that our explanation and any mathematical model of a complex system, such as the nervous system in the image, will need to be complex.  In mathematical terms, this will probably mean a non-linear dynamic model with a solution in the form of a phase portrait.  ‘Non-linear’ because the response of the system not proportional to the stimulus inducing the response; ‘dynamic’ because the system changes with time; and a ‘phase portrait’ because the system can exist in many states, some stable and some unstable, dependent on its prior history; so, for instance for a pendulum, its phase portrait is a plot of all of its possible positions and velocities.

If all this sounds too hard, then you see why people shy away from using complex models to describe a complex system even when it is obvious that the system is complex and extremely unlikely to be adequately described by a linear model, such as for the nervous system in the image.

In other words, if we can’t see it and its too hard to think about it, then we pretend it’s not happening!


The thumbnail shows an image of a fruit-fly’s nervous system taken by Albert Cardona from HHMI Janelia Research Campus.  The image won a Wellcome Image Award in 2015.

William C. Wimsatt, Randomness and perceived randomness in evolutionary biology, Synthese, 43(2):287-329, 1980.

For more on this topic see: ‘Is the world comprehensible?‘ on March 15th, 2017.


Everyday examples contribute to successful learning

Some weeks ago I quoted Adams and Felder [2008] who said that the ‘educational role of faculty [academic staff] is not to impart knowledge; but to design learning environments that support…knowledge acquisition’ [see ‘Creating an evolving learning environment’ on February 21st, 2018].  A correspondent asked how I create a learning environment and, in response, this is the first in a series of posts on the topic that will appear every third week.  The material is taken from a one-day workshop that Pat Campbell [of Campbell-Kibler Associates] and I have given periodically in the USA [supported by NSF ] and UK [supported by HEA] for engineering academics.

Albert Einstein is reputed to have said that ‘knowledge is experience, everything else is just information’.  I believe that a key task for a university teacher of engineering is to find the common experiences of their students and use them to illustrate engineering principles.  This is relatively straightforward for senior students because they will have taken courses or modules delivered by your colleagues; however, it is more of a challenge for students entering the first year of an engineering programme.  Everyone is unique and a product of their formative conditions, which makes it tricky to identify common experiences that can be used to explain engineering concepts.  The Everyday Engineering Examples, which feature on a page of this blog [https://realizeengineering.blog/everyday-engineering-examples/], were developed to address the need for illustrative situations that would fall into the experience of most, if not all, students.  Two popular examples are using the splits in sausages when you cook them to illustrate two-dimensional stress systems in pressure vessels [see lesson plan S11] and using a glass to extinguish a birthday candle on a cup cake to explain combustion processes [see lesson plan T11].

Everyday Engineering Examples were developed as part of an educational research project, which was funded by the US National Science Foundation [see ENGAGE] and demonstrated that this approach to teaching works.  The project found that significantly more students rated their learning with Everyday Engineering Examples as high or significant than in the control classes independent of the level of difficult involved [Campbell et al. 2008].  So, this is one way in which I create a learning environment that supports knowledge acquisition.  More in future posts…


Adams RS & Felder RM, Reframing professional development: A systems approach to preparing engineering educators to educate tomorrow’s engineers. J. Engineering Education, 97(3):230-240, 2008

Campbell PB, Patterson EA, Busch Vishniac I & Kibler T, Integrating Applications in the Teaching of Fundamental Concepts, Proc. 2008 ASEE Annual Conference and Exposition, (AC 2008-499), 2008


CALE #1 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA]