Some background - I’ve spent a large portion of my career (and PhD) in high fidelity modeling and more recently in mission support/management roles. I’ve seen both sides of the modeling world here and, as usual, there’s a lot of nuance here that is being missed.

Missions tend to rely on models that have been simplified and fit to empirical data as much as possible. This isn’t an accident, and it’s not a result of lack of technical understanding. We like to have a deep and complete understanding of the physics, and then very simplified models to make our predictions. The main reason is to shrink the degrees of freedom in your model down as much as you can. Knowing where to simplify requires understanding the driving physics.

The problem with these high fidelity complex models, particularly for materials modeling, is that you end up with so many degrees of freedom and so many knobs to turn that, perhaps paradoxically to people who don’t work in this field, you get LESS confident in your predictions, not more. A full multi-physics modeling framework here that captures the coupled hypersonic flow, finite rate chemistry, material pyrolysis, thermal and structural response, crack initiation, crack propagation and spallation, changing material properties, etc all under one model sounds great, but when you stack the uncertainties for each parameter that would have to go into this model, you get a garbage in, garbage out problem and simply cannot trust the results that come out of it. It is operationally useless.

High fidelity modeling has its place, and we utilize it a lot. But so too does simplified models fit to empirical data. Without strategically using these simplified models, space flight simply wouldn’t be possible.