| > Science needs models based on mechanistic understanding of the underlying phenomena. A model that merely predicts is useful for engineers, not scientists I'm not sure I agree. I'm aware of quite a bit of supercomputing time that is spent doing lattice QCD calculations (which apparently some scientists find useful), and though I'm no quantum physicist I'm pretty sure there is not much of a "mechanistic understanding" in QCD. I think your claim also doesn't apply to a lot of social science - psychology has a lot of functional models, but I don't think there are many mechanisms described. I'll also state that modern science that doesn't require any engineering is pretty rare nowadays, so if a predictive model helps engineers that can then help scientists, the model has been helpful to scientists. Ohm's law existed long before there was a mechanistic description behind it, and though it is mostly used for "engineering," I feel confident that a lot of scientists in the 19th century found it useful. From https://www.olcf.ornl.gov/leadership-science/physics/: "New Frontiers for Material Modeling via Machine Learning Techniques" - 40,000 hours allocated on Summit "Large scale deep neural network optimization for neutrino physics" - 58,000,000 hours allocated on Summit. Supercomputers typically do not allocate 58 million hours to things which are not useful. |