[universa1]

in some sense yes, and if you are only interested in good predictions this might work out well. What, maybe due to my limited understanding, is, that this is not theory driven and therefore does not really provide understanding of the underlying process.

[nico]

> and therefore does not really provide understanding of the underlying process

What is “the underlying process”?

For example, Newton was able to model gravity quite successfully without ever being able to “understand the underlying process”. In fact, physics today still doesn’t have a good grasp on what gravity is. Yet we use the models and equations all the time

In a way, physics is also a collection of black boxes, perhaps just seemingly more elegant boxes

[SaberTail]

Gravity can be modeled as "things on the earth's surface accelerate downward at about 10 m/s², regardless of their mass". This works very well. Gravity can be modeled as "planets orbit the sun according to Kepler's laws". This also works very well.

Newton realized that these phenomena could be explained as arising from the same underlying process of an inverse square law. This is a much more useful model, and allows predictions that allow us to do things like space flight, even if it is not complete.

[lainga]

> What is “the underlying process”?

IMO -

The simple ones: advection, latent heat release/absorption from water changing phases, and the Coriolis force. If you need an AI for this, please take a course on differential equations.

The hard ones: droplet/ice crystal formation, cloud feedback on radiative transfer, evaporation at air-sea boundaries. If you can train a model for these processes, please, please tell someone.

[jprete]

Physics wants to open the black boxes until it can no longer figure out how to pry the remaining boxes open!

It's not useful to draw a false equivalence between AI-style "the model predicts, that's good enough" and science as a whole which cares very much about the underlying structure.

[MyFirstSass]

Wouldn't you be able to somehow couple it with another model that takes the NN data and somehow untangles it's convoluted logic into an isomorphic human readable equation, ie. a model that has one task and that is translating NN logic into human equations.

The training data could be real physics in a simulator held up against evolutionary driven AI logic that competes against it with various goals that are then evaluated and if given a high score then marked as isomorphic and given enough runs you'd get a dataset.

[nico]

Very cool idea! I wonder how simple could the NNs be to model some basic physical process?

[Mathnerd314]

I think at some point it is worth admitting that there are variables you can't account for. Like the precise geography - the models model an area 1 mile square as a single vector, maybe even more coarse. They don't model every tree, rock, and bush. In a neural net you can just have "weight goop" which accounts for the net effect of these unmodeled features, but in a traditional model adding "fudge factors" and extrapolating back from the model to points of interest is tricky.

[bongodongobob]

I think we understand the processes just fine. The issue is that we can't get a "closed form" solution for 10^100 interactions per planck time.

[gowld]

That's like saying chemistry has nothing to understand because we know Schrodinger's wave equation. Or that we understand biology and psychology just for for the same reason.