Many of our existing physical models can be decomposed into "high-confidence, well tested bit" plus "hand-wavy empirically fitted bit". I'd like to see progress via ML replacing the empirical part - the real scientific advancement then becomes steadily reducing that contribution to the whole by improving the robust physical model incrementally. Computational performance is another big influence though. Replacing the whole of a simulation with an ML model might still make sense if the model training is transferrable and we can take advantage of the GPU speed-ups, which might not be so easy to apply to the foundational physical model solution. Whether your model needs to be verified against real physical models depends on the seriousness of your use-case; for nuclear weapons and aerospace weather forecasts I imagine it will remain essential, while for a lot of consumer-facing things the ML will be good enough.