Sister blog of Physicists of the Caribbean. Shorter, more focused posts specialising in astronomy and data visualisation.

Tuesday 28 August 2018

Explain yourself !

Ooo-eck. Lots of implications here.

The next step of this shift away from purely mathematical modeling is already on the way: Physicists now custom design laboratory systems that stand in for other systems which they want to better understand. They observe the simulated system in the lab to draw conclusions about, and make predictions for, the system it represents.

The best example may be the research area that goes by the name “quantum simulations.” These are systems composed of interacting, composite objects, like clouds of atoms. Physicists manipulate the interactions among these objects so the system resembles an interaction among more fundamental particles. For example, in circuit quantum electrodynamics, researchers use tiny superconducting circuits to simulate atoms, and then study how these artificial atoms interact with photons.

These simulations are not only useful to overcome mathematical hurdles in theories we already know. We can also use them to explore consequences of new theories that haven’t been studied before and whose relevance we don’t yet know.

Quantum simulations also make us wonder what it means to explain the behavior of a system to begin with. Does observing, measuring, and making a prediction by use of a simplified version of a system amount to an explanation?

But for me, the most interesting aspect of this development is that it ultimately changes how we do physics. With quantum simulations, the mathematical model is of secondary relevance. We currently use the math to identify a suitable system because the math tells us what properties we should look for. But that’s not, strictly speaking, necessary. Maybe, over the course of time, experimentalists will just learn which system maps to which other system, as they have learned which system maps to which math. Perhaps one day, rather than doing calculations, we will just use observations of simplified systems to make predictions.

I dunno, I'd say an explanation requires a description of the physical process. If you've just got a prediction (i.e. conventionally just a numerical value) you haven't got a physical understanding at all. Like how Maxwell had his crazy vortex theory that gave identical mathematical values for EM forces but gave way to something much easier for the rest of us to understand, the maths is not the model. If I've got a physical model that makes a prediction, but I don't understand how the physical model works, what use is that ? It's of no more benefit than a mathematical model I can't interpret. Do you need a physical description ? Perhaps not, but dammit, I want one. Can't see the point in making predictions I don't understand, that sort of defeats the whole purpose.

https://www.quantamagazine.org/the-end-of-theoretical-physics-as-we-know-it-20180827/?mc_cid=78b85a8581&mc_eid=ded24b5349

1 comment:

  1. I have serious doubts about all this. I've seen systems developed against mock endpoints - which were nicely created so as to recreate certain aspects of the real world.

    They aren't the real world. Just saying.

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