Agreed. Though I'd say that model errors are a challenge, but I wouldn't call them insidious. There is generally serious analysis of the effect of the chosen di

Agreed. Though I'd say that model errors are a challenge, but I wouldn't call them insidious. There is generally serious analysis of the effect of the chosen discretization method/grid size, how to handle sub-grid scale effects, etc. Likewise models. I think it is interesting to compare this old panic with ML work. Without gazillions of flops the neural network approach was dead. After a few years it became clear that float/double precision was useless, and now for certain problems half precision is standard. Where one resource (people, CPU, ram, money) is the fixed constraint, other factors will be tweaked to compensate.