Tag Archives: simulation

Opal offers validation opportunity for climate models

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OrangeFanSpongeSmallMany of us will be familiar with the concept of the carbon cycle, but what about the silicon cycle?  Silicon is the second most abundant element in the Earth’s crust.  As a consequence of erosion, it is carried by rivers into the sea where organisms, such as sponges and diatoms (photosynthetic algae), convert the silicon in seawater into opal that ends up in ocean sediment when these organisms die.  This marine silicon cycle can be incorporated into climate models, since each step is influenced by climatic conditions, and the opal sediment distribution from deep sea sediment cores can be used for model validation.

This approach can assist in providing additional confidence in climate models, which are notoriously difficult to validate, and was described by Katharine Hendry, a Royal Society University Research Fellow at the University of Bristol at a recent conference at the Royal Society.  This struck me as an out-of-the box or lateral way of seeking to increase confidence in climate models.

There are many examples in engineering where we tend to shy away from comprehensive validation of computational models because the acquisition of measured data seems too difficult and, or expensive.  We should take inspiration from sponges – by looking for data that is not necessarily the objective of the modelling but that nevertheless characterises the model’s behaviour.

Source:

Thumbnail: http://www.aquariumcreationsonline.net/sponge.html

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.

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

Emergent inequality

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115-1547_IMGI wrote a few weeks ago about my visit to a conference on high-performance computing and big data [see ‘Mining Data‘ on February 12th, 2014].  We are able to use high performance computers to create simulations of complex engineering systems before we embark on the usual costly, and sometimes catastrophic, construction of the real system.  Some complex systems exhibit emergent behaviour, meaning that although we understand and can model the individual components when we connect them together the system behaves a new and unexpected manner, which is why it is good practice to simulate a system before building it.  Manuel Delanda has written eloquently on the topic of emergence in simulations in The Emergence of Synthetic Reason.  I encourage my first year thermodynamics students to read at least the first chapter which an amazing tour-de-force that ranges effortless from spontaneous flows of energy at the molecular level to the formation of thunderstorm systems.

Nature has many systems that could be described as emergent at some level or other.  For instance, the ants in an anthill go about their simple interactions but have no idea about how the anthill works or, perhaps more amazingly, the rafts that an ant colony can form using their bodies during a flood, as shown in recent research by Jessica Purcell and her co-workers at the University of Lausanne. With the exception of the queen, there is no leader in an anthill and all of the ants appear to be equal.  The same is not true in human society where currently 1% of the population own nearly half of the world’s wealth.

Seven out of ten people live in a country where inequality has increased in the last 30 years according to a recent Oxfam report.  This is bad news for everyone, including the wealthy because Richard Wilson and Kate Pickett have shown that in developed countries, there is a correlation between the incidences of mental illnesses and the level of income difference between the rich and poor.  A more recent study of the US found that depression was more common in states with greater income inequality, after taking account of age, income and educational differences.   Wilson and Pickett conclude that we become less nice and less happy people in more unequal societies regardless of our position in the social spectrum.

Sources:

http://in.reuters.com/article/2013/10/09/creditsuisse-wealth-idINL6N0HZ0MD20131009

http://opinionator.blogs.nytimes.com/2014/02/02/how-inequality-hollows-out-the-soul/

http://www.huffingtonpost.com/2013/11/01/income-inequality-depression_n_4190926.htm

http://www.huffingtonpost.com/winnie-byanyima/a-plan-for-tackling-inequ_b_4768096.html