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Comment by troupo | Hacker News Reader

> I’m actually surprised to hear that the standard approaches used for weather forecasts do not use AI.

Why would they use AI where weather models exist, and work? What exactly would AI bring into space?

I mean as we're seeing right here, they are just as good in their testing stage while not taking several hours to run. They're more efficient to run and calculate since they don't need to go through a lengthy process to determine the weather, just take advantage of the cyclic nature of nature to do the work for us.

The problem is that we are entering an era of rapid climate shift. Relying primarily on historical data doesn't work that well when you are trying to predict unprecedented weather events. The laws of physics, on the other hand, have not changed - so a simulation will still work just fine.

No we aren't actually. We are still well within historical data, since we use that still to calculate future weather patterns, and there hasn't been anything unprecedented that would trip up a model like this either.

So global warming is just a hoax? Or what are you saying?

No, what they are saying is events are not so far out of bounds that the models fall apart. Either there have been no black swans spotted yet, or in conjunction with other numerical models the outliers are correctly predicted.

No. What they are saying is that:

- weather is not climate

- we are not in a situation where weather models don't work

A lot of people are using neural network models to model fields in variable resolution, solve PDEs and such. I know a bit about astrophysical modeling and you sure can't afford to model every part of a star in the same amount of detail. You can pick some special coordinate system or make the grid vary in size or you can use a neural network to model some f(x,y,z) and the network can allocate its own resources to put more detail where you need it.

https://arxiv.org/abs/2102.11802

PINNs are neat but by my understanding they’re conceptually not too different from traditional numerical solution methods. The solution to your PDE is some complicated function that is not available in closed-form, so you require a numerical approximation of that function. Traditional methods use various kinds of grid discretization systems (possibly with interpolation schemes). PINNs replace the grid with a parametric continuous function. I’m not sure how PINNs handle discontinuities like shocks though.

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