What is so special about Warp?

[sreeaka]

I looked through 'Mixed Elasticity' example. It is implementation of galearkin finite element method in python. What is special about warp in comparison with any other finite element software out there. I am asking this out of both ignorance and curiosity. Thanks

[momo-van]

@sreeaka Warp is not a FEM software package. It is a Python DSL, essentially a thin abstraction layer over CUDA, that lets you write differentiable kernels in pure Python. That makes it a strong fit for computational physics and geometry processing workloads. It also interoperates well with the broader GPU Python ecosystem, including JAX and PyTorch, which makes it especially useful for combining physics with AI, for example writing the solver in Warp and the optimization or training loop in PyTorch. More on positioning and application domains here: https://developer.nvidia.com/warp-python

Warp also includes a few domain-specific toolkits, such as Warp Sparse and Warp FEM. Warp FEM is better seen as a reference implementation that lowers the barrier to entry, not as a rigid statement that this is the way FEM must be done in Warp. You can absolutely use Warp itself to write FEM code directly, and we provide a number of examples to help users get started in various physics domains.

A strong example of Warp accelerating computational physics, especially in multibody dynamics, is Newton: https://github.com/newton-physics/newton

[momo-van]

You might also find this blog (focused on CFD) generally useful from Warp positioning perspective. There are some adoption/acceleration examples at the end

https://developer.nvidia.com/blog/build-accelerated-differentiable-computational-physics-code-for-ai-with-nvidia-warp/

[momo-van]

@gdaviet for viz and comment, especially on warp.fem

[gdaviet]

Thanks @momo-van, that is a great summary. warp.fem is a toolkit that aims to provide the ease of use of typical finite element libraries (Galerkin/Discontinuous Galerkin), but leveraging Warp to provide GPU-native performance and built-in auto-differentiability, in particular. It is a pure Python/Warp module, so any of its components could be reimplemented in raw Warp, if needed.