[foxes]

Better, why do people think they can predict long term dynamics of what is basically a chaotic system? I think finding some small local dynamics is fine, but applying a neural network to try say something about global long term dynamics complete garbage - ie long term weather simulation, stock markets etc.

Chaotic systems can be deterministic, just that you will never be able to accurately measure all the variables to make a long term prediction accurately.

In the weather example, people know the equations that approximate how it works. Why value does a neural network bring? Knowing the equations is better understanding.

[jkabrg]

There was a Quanta Magazine article talking about predicting the evolution of a flame-front using ML. The ML algorithm remained accurate for eight Lyapunov intervals; eight times longer than the previous SOTA.

https://www.quantamagazine.org/machine-learnings-amazing-abi...

[foxes]

The only real argument I think you can make is that it might be more efficient to have a neural network quickly spit out an approximate solution instead of solving the actual equations.

But if you have the time having the actual equations is more valuable?

[PeterisP]

Assuming that "having the actual equations" is possible, which in market predictions it most likely is not.