[partitioned]

This is simply not true and an uninformed opinion of what modeling physical events with AI looks like.

You seem to assume it’s a purely data-driven approach, it is not. You could use a physics informed neural network (PINN) that utilizes both approaches. That is, it uses both historical data (and likely synthetic data from physics models), as well as physical equations in the loss function (in this case atmospheric as fluid equations) as part of the training. It can truly be the best of both worlds if approached correctly.

That being said, 99% of AI out there is just masters thesis level data in -> prediction out, but that is far from what the useful AI models that are currently being developed to predict and forecast dynamical physical systems.

Additionally, you can generate synthetic data with the physical models of “edge” and “tail” events to train the model on. This by itself allows the ML model to be able to model almost all events that we can physically model, so at its base it’s at least as useful as the big O order models we use while being orders of magnitude faster. This doesn’t even account for using the physical equations to assist in training the model directly (through architecture tricks or in the loss function).

Source: I work on AI models that merge data and physics for dynamical physical systems

[Majromax]

> That being said, 99% of AI out there is just masters thesis level data in -> prediction out, but that is far from what the useful AI models that are currently being developed to predict and forecast dynamical physical systems.

I think one surprising outcome of the recent wave of ML-based weather forecasting models is just how accurate the "dumb" approaches are. Pangu-Weather and a couple of its peers are essentially vision transformers without much explicit physics-informed regularization.

[mjburgess]

If you have explanatory models constraining the space of possible function fits, etc. etc. then I concede the point -- though, I rather regard it as my point.

The comment I replied to used "AI" in its generic sense which I take to name the theory-free frequentist stats currently in vogue. I don't regard theories as AI -- so adding physics to a NN is, in large part, computational physics. You can call it "AI", but then so-goes any use of a computer model of any kind.

[partitioned]

Well, the difference is the data-driven aspect of parts of the model. While its constrained by physics during the learning process it isn't just running a forward physics model to get the solution. The upfront computational load and extremely fast inference times through parameterization IS what makes it AI, and what makes it useful versus a normal numerical computer model.