Yeah, that’s pretty impressive. I wonder if you could apply the same philosophy in other areas too. Instead of training the model with data produced in a simulation, you could just feed it real world data instead. Like, if you gave a bunch of stress-strain data to a model, could you make better predictions about the behavior of physical structures, such as bridges and towers.

Physical simulations factor in a dozen or so weather conditions to predict outcomes.

Many more parameters than that.

Machine learning can track thousands of conditions

Scientists already know which ones are relevant. You're not going find any big surprises there with an AI. Shotgun-style factor analysis has already been done to death. The price of baked beans doesn't impact the wind direction in the Persian Gulf. It's OK to not consider it.

drawing connections not realized in physical models

Again, it's possible but unlikely. And you'd need an AI that could be queried to tell you what factors it considered, and most of them don't work that way right now.

Statistical models don't become more accurate because you throw irrelevant parameters at them. But that's how ML systems work.