[szvsw]

Awesome! Several of my colleagues are working on differentiable physics simulations (mostly FEM type stuff for structural design optimization) so I’m excited to share this with them! They mostly work in Julia. My own experiments with auto-diff’d physics sims have been in Python (specifically, Taichi for the JIT/GPU acceleration or occasionally PyTorch/Jax).

Can you talk a little bit about the challenges of bringing something like what you’ve implemented to existing autograd engines/frameworks (like the ones previously mentioned)? Are you at all interested in exploring that as a mechanism for increasing access to your methodology? What are your thoughts on those autodiff engines?

[frankling_]

We actually did some preliminary experiments with Taichi hoping to benefit from the GPU parallelization. I think generally, the world of autodiff tooling is in very good shape. For anything non-exotic, we just use JAX or Torch to get things done quickly and with good performance.

Generally, integrating the ideas behind DiscoGrad into existing frameworks has been on our mind since day one, and the C++ implementation represents a bit of a compromise made to have a lot of flexibility during development while the algorithms were still a moving target, and good performance (albeit without parallelization and GPU support as of yet). Based on DiscoGrad's current incarnation, however, it should not be terribly hard to, say, develop a JAX+DiscoGrad fork and offer some simple "branch-like" abstraction. While we've been looking into this, it can be a bit tricky in a university context to do the engineering leg work required to build something robust...

[szvsw]

> While we've been looking into this, it can be a bit tricky in a university context to do the engineering leg work required to build something robust...

I definitely hear you on this! As a grad student who is one of the only developers with actual professional dev xp in my lab, it can be brutal being tasked with turning academic spaghetti code into something semi-productionized/robust.

[avibryant]

Do you have any links to your experiments and/or those of your colleagues?

[szvsw]

Emailed you through the email listed on your HN profile