tiny-differentiable-simulator VS RustyNEAT

I need differentiable rigid body dynamics because I want to do nonlinear MPC. One library that can do this is C++ is Tiny Differentiable Simulator https://github.com/erwincoumans/tiny-differentiable-simulator. As I understand it, this software uses a C++ auto-diff library and code generation to create CUDA kernels to compute fast derivatives in parallel. This seems pretty fast because it's C++. Another option is Brax https://github.com/google/brax. Brax uses JAX which I've never used, but from what I've seen online, JAX is popular for researchers and probably very good.

https://github.com/google-research/tiny-differentiable-simul...

Yes, Chrome about://tracing is great to visualize your custom timing data. Happy used for the last 5 years in Bullet and recent physics engines, including events across tracing multiple threads:

https://github.com/google-research/tiny-differentiable-simul...

Or our C++ and CUDA Tiny Differentiable Simulator: https://github.com/google-research/tiny-differentiable-simulator

Drake (and also tiny-differentiable-simulator that I know of) are using meshcat and it seems neat to me

If you want to really speed up your environment several orders of magnitude, you can implement it in cuda/vulkan /opencl. Here is an example of what I did in vulkan https://mobile.twitter.com/MendozaDrosik It allows me to stimulate thousands of agents in parallel. Works wonders especially if you want to use genetic algorithms. If you're interested, I might make python bindings to my minecraft environment. If you write in rust (like I do), then you can add python bindings very easily with PyO3. This is what I did here https://github.com/aleksander-mendoza/RustyNEAT/blob/main/rusty_neat_quick_guide.py (it's GPU accelerated implementation of NEAT algorithm)