[gouh]

We don't necessarily create complex simulations to understand them, but to have those simulation available to then build for e.g reinforcement learning models.

To take a simple example, if you have a high-school wagon-rolling-down-a-slope Newton Physic exercise, then you can capture in your mind everything which explains the problem. But then take https://youtu.be/a3jfyJ9JVeM , it's still Newton, but so many constrains that we cannot picture in our mind everything and we are not even solving the problem with a close form equation anymore. But we have modeled a simulation which enable us to make experiments and build locomotion algorithm. Actually, you don't even need to understand Newton or Physics to implement that paper, you can just see it as optimizing a black box.

In the future I could see that happen at larger scale. For eg we put everything we known about the brain or cells in a simulation, we can't picture what's really going on because the system is too complex, yet we can run simulations and optimize models to find for eg new cures, treatments, etc.