physics-simulation

Dear Brax team,

Since Brax is fully differentiable, I thought it'd be possible to use it like DiffTaichi or GradSim for system identification (e.g. determining the mass of an object from a trajectory and known force) but I couldn't find any example for this.
Do you happen to have any demo or tips for this?

From the top of my head I would do something like this:
Let's say the task is to es

Would it be possible to add the Origin Rebase feature that is built into Physx 4.x and up into Echo?

This would enable larger worlds to be made without floating point precision errors. Here is an example of what can happen if you are too far away from origin. https://gyazo.com/a96db2d1a4ef8ff8935d2054597e151b

It would be nice if we could enable it in project settings and it was just part of

Hi,

one could and should experiment with Interprocedural optimization (IPO) also known as link-time optimization (LTO), especially on the host side for smaller binaries and potentially faster code. It's supported by GCC, Clang, and ICC, among others, which are our typical go-to compilers in HPC.

It's very easy to implement as well

(Up for grabs for anyone who wants to do this)

Docpages generated for flax modules such as our models (RBM, RBMSymm, DenseSymm) are somehow... confused.
That's because Flax modules have a lot of methods scattered around and the attributes get put a bit everywhere.

Moreover, attributes of the model/layer itself are mixed with attributes common to all flax modules that are not really relevan

This issue is a roadmap for Molly.jl development. Feel free to discuss things here or submit a PR. Bear in mind that significant refactoring will probably occur as the package develops.

Low hanging fruit

Want to get involved? These issues might be the place to start:

• Speedups to the code. Both on the level of individual functions and the overall algorithm. [Getting there]
• Look over

The library needs tests to prevent bugs. This library uses pytest as test suite, which is by far the most user friendly test-suite for Python. This is great, because this means anyone can add tests; it's super easy! Moreover, it makes you acquainted with the code, which is why it's labeled a good first issue.

When adding tests, keep the following in mind: