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Comment by ThereIsNoWorry | Hacker News Reader

As others pointed out, this is just a fallacy. Physics (as well as Math, CS, and all other disciplines) is full of non-linear, chaotic problems that are way too hard to solve, so academia doesn't like to teach them to new students and even experts aren't delighted to deal with them, because it's frustrating that we can't solve them (in a "beautiful" and "elegant" way). My own hot take is that Physics is stuck on a theory of everything for the last 100 years, because we are stuck on some fundamental limitations what Math is able to model.

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>My own hot take is that Physics is stuck on a theory of everything for the last 100 years, because we are stuck on some fundamental limitations what Math is able to model.

Your previous assumption reads more like math being able to model it, but scientists preferring elegant, aesthetic and simplistic solutions.

Looking at my PhD physics neighbor in student housing a few years back, it doesn't seem true though. He was juggling multi-page equations for his quantum field theory thesis.

In control engineering it's more true though. I remember one of my professors say that many people only use linear controls because there they can "prove" the stability of the system. In contrast to the nonlinear, i.e. everything else, control theory where such proofs were harder to get by.

However, he continued, proving the stability of a linear controller interacting with a linearized system could never "prove" the system's stability as a whole.

From experience linear controls is probably more popular because there's a larger set of tools in the toolbox, so to speak, and the nature of the systems is that this is (usually) close enough for modelling nonlinear systems which are close enough to linear (in fact you may introduce nonlinear terms in your controller to try to remove nonlinearities in your system). It's not just having a 'proof' of stability, it's having methods to analyse the system and optimise the controller, which are much more difficult to apply in general to non-linear systems. It's much like how engineers will generally just stick to classical models of physics despite them being 'wrong', because in practice they are close enough and also much easier to deal with.

> because we are stuck on some fundamental limitations what Math is able to model.

This seems more a limitation of what (human) mathematicians are able to model, rather than something fundamental in math itself. Perhaps in a few centuries we will have invented mathematical tools that allow the mind to grasp nonlinear systems better, similar to how the invention of better construction tooling has enabled the construction of ever larger buildings.

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