For example, optimizing the shape of an aerofoil, wing, wind turbine, ship, pump, etc. The "real" data is the result of computationally costly fluid dynamics simulations. The network uses as much of those as it likes to modify the design to be better.
There are also a lot of problems where there is nearly limitless data (ie. > 10^12 bytes of data). Examples would be MRI/CT scans, all internet video, all TV channels, etc.
>For example, optimizing the shape of an aerofoil, wing, wind turbine, ship, pump, etc. The "real" data is the result of computationally costly fluid dynamics simulations.
I'm doing something similar at the moment. The data is not limitless. It's in fact quite expensive.
In most cases you still need to generate the large number of simulations in your training set, and the simulations are computationally expensive to do. If you're doing it on a metered platform you can tie the cost of the data set directly to the number of cpu hours need to generate it, and from there to a dollar value.