Tag Archives: simulation

Virtual digitalism

Decorative image of 10 micron spheres in nanoscopeSome months ago I wrote about the likelihood that we are in a simulation [see ‘Are we in a simulation?‘ on September 28th, 2022] and that we cannot be sure whether are or not.  For some people, this will raise the question that if we are in a simulation, then what is real?  In his book, Reality+, David J Chalmers provides a checklist of properties possessed by real things, namely: existence, causal powers, mind-independence, non-illusoriness and genuineness.  The possession of these properties could be established by answering the five questions in the box below and we would expect real objects to possess one or more of these properties.  Objects that are found in a virtual world generated by a simulation are real objects because they have at least one, and often many of these properties, such as causal powers and independence from our minds.  We can consider them to be digital objects, or structures of binary information or bits.  This leads to a form of the ‘It-from-bit’ hypothesis because it implies that molecules are made of atoms, atoms are made of quarks, and quarks are made of bits – unless of course we are not in a simulation but we will probably never know for certain.

Source: David J Chalmers, Reality+: virtual worlds and the problems of philosophy, Penguin, 2022.

Image shows a self-assembly of 10 micron spheres viewed out-of-focus in bright-light optical microscope.

Storm in a computer

Decorative painting of a stormy seascapeAs part of my undergraduate course on thermodynamics [see ‘Change in focus’ on October 5th, 2022) and in my MOOC on Thermodynamics in Everyday Life [See ‘Engaging learners on-line‘ on May 25th, 2016], I used to ask students to read Chapter 1 ‘The Storm in the Computer’ from Philosophy and Simulation: The Emergence of Synthetic Reason by Manuel Delanda.  It is a mind-stretching read and I recommended that students read it at least twice in order to appreciate its messages.  To support their learning, I provided them with a précis of the chapter that is reproduced below in a slightly modified form.

At the start of the chapter, the simplest emergent properties, such as the temperature and pressure of a body of water in a container, are discussed [see ‘Emergent properties’ on September 16th, 2015].  These properties are described as emergent because they are not the property of a single component of the system, that is individual water molecules but are features of the system as a whole.  They arise from an objective averaging process for the billions of molecules of water in the container.  The discussion is extended to two bodies of water, one hot and one cold brought into contact within one another.  An average temperature will emerge with a redistribution of molecules to create a less ordered state.  The spontaneous flow of energy, as temperature differences cancel themselves, is identified as an important driver or capability, especially when the hot body is continually refreshed by a fire, for instance.  Engineers harness energy gradients or differences and the resultant energy flow to do useful work, for instance in turbines.

However, Delanda does not deviate to discuss how engineers exploit energy gradients.  Instead he identifies the spontaneous flow of molecules, as they self-organise across an energy gradient, as the driver of circulatory flows in the oceans and atmosphere, known as convection cells.  Five to eight convections cells can merge in the atmosphere to form a thunderstorm.  In thunderstorms, when the rising water vapour becomes rain, the phase transition from vapour to liquid releases latent heat or energy that helps sustain the storm system.  At the same time, gradients in electrical charge between the upper and lower sections of the storm generate lightening.

Delanda highlights that emergent properties can be established by elucidating the mechanisms that produce them at one scale and these emergent properties can become the components of a phenomenon at a much larger scale.  This allows scientists and engineers to construct models that take for granted the existence of emergent properties at one scale to explain behaviour at another, which is called ‘mechanism-independence’.  For example, it is unnecessary to model molecular movement to predict heat transfer.  These ideas allow simulations to replicate behaviour at the system level without the need for high-fidelity representations at all scales.  The art of modelling is the ability to decide what changes do, and what changes do not, make a difference, i.e., what to include and exclude.

Source:

Manuel Delanda Philosophy and Simulation: The Emergence of Synthetic Reason, Continuum, London, 2011.

Image: Painting by Sarah Evans owned by the author.

Nudging discoveries along the innovation path

Decorative photograph of a Welsh hillThe path from a discovery to a successful innovation is often tortuous and many good ideas fall by the wayside.  I have periodically reported on progress along the path for our novel technique for extracting feature vectors from maps of strain data [see ‘Recognizing strain‘ on October 28th, 2015] and its application to validating models of structures by comparing predicted and measured data [see ‘Million to one‘ on November 21st, 2018], and to tracking damage in composite materials [see ‘Spatio-temporal damage maps‘ on May 6th, 2020] as well as in metallic aircraft structures [see ‘Out of the valley of death into a hype cycle‘ on February 24th 2021].  As industrial case studies, we have deployed the technology for validation of predictions of structural behaviour of a prototype aircraft cockpit [see ‘The blind leading the blind‘ on May 27th, 2020] as part of the MOTIVATE project and for damage detection during a wing test as part of the DIMES project.  As a result of the experience gained in these case studies, we recently published an enhanced version of our technique for extracting feature vectors that allows us to handle data from irregularly shaped objects or data sets with gaps in them [Christian et al, 2021].  Now, as part of the Smarter Testing project [see ‘Jigsaw puzzling without a picture‘ on October 27th, 2021] and in collaboration with Dassault Systemes, we have developed a web-based widget that implements the enhanced technique for extracting feature vectors and compares datasets from computational models and physical models.  The THEON web-based widget is available together with a video demonstration of its use and a user manual.  We supplied some exemplar datasets based on our work in structural mechanics as supplementary material associated with our publication; however, it is applicable across a wide range of fields including earth sciences, as we demonstrated in our recent work on El Niño events [see ‘From strain measurements to assessing El Niño events‘ on March 17th, 2021].  We feel that we have taken some significant steps along the innovation path which will lead to adoption of our technique by a wider community; but only time will tell whether this technology survives or falls by the wayside despite our efforts to keep it on track.

Bibliography

Christian WJR, Dvurecenska K, Amjad K, Pierce J, Przybyla C & Patterson EA, Real-time quantification of damage in structural materials during mechanical testing, Royal Society Open Science, 7:191407, 2020.

Christian WJ, Dean AD, Dvurecenska K, Middleton CA, Patterson EA. Comparing full-field data from structural components with complicated geometries. Royal Society open science. 8(9):210916, 2021

Dvurecenska K, Graham S, Patelli E & Patterson EA, A probabilistic metric for the validation of computational models, Royal Society Open Science, 5:1180687, 2018.

Middleton CA, Weihrauch M, Christian WJR, Greene RJ & Patterson EA, Detection and tracking of cracks based on thermoelastic stress analysis, R. Soc. Open Sci. 7:200823, 2020.

Wang W, Mottershead JE, Patki A, Patterson EA, Construction of shape features for the representation of full-field displacement/strain data, Applied Mechanics and Materials, 24-25:365-370, 2010.

Are we in a simulation?

Decorative photograph of trains at terminusThe concept of digital twins is gaining acceptance and our ability to generate them is advancing [see ‘Digital twins that thrive in the real-world’ on June 9th, 2021].  It is conceivable that we will be able to simulate many real-world systems in the not-too-distant future.  Perhaps not in my life-time but possibly in this century we will be able to connect these simulations together to create a computer-generated world.  This raises the possibility that other forms of life might have already reached this stage of technology development and that we are living in one of their simulations.  We cannot know for certain that we are not in a simulation but equally we cannot know for certain that we are in a simulation.  If some other life form had reached the stage of being able to simulate the universe then there is a possibility that they would do it for entertainment, so we might exist inside the equivalent of a teenager’s smart phone, or for scientific exploration in which case we might be inside one of thousands of simulations being performed simultaneously in a lab computer to gather statistical evidence on the development of universes.  It seems probable that there would be many more simulations performed for scientific research than for entertainment, so if we are in a simulation then it is more likely that the creator of the simulation is a scientist who is uninterested in this particular one in which we exist.  Of course, an alternative scenario is that humans become extinct before reaching the stage of being able to simulate the world or the universe.  If extinction occurs as a result of our inability to manage the technological advances, which would allow us to simulate the world, then it seems less likely that other life forms would have avoided this fate and so the probability that we are in a simulation should be reduced.  You could also question whether other life forms would have the same motivations or desires to create computer simulations of evolutionary history.  There are lots of reasons for doubting that we are in a computer simulation but it does not seem possible to be certain about it.

David J Chalmers explains the probability that we are in a simulation much more elegantly and comprehensively than me in his book Reality+; virtual worlds and the problems of philosophy, published by Penguin in 2022.