>One of the domains where I hope Julia will excel is precisely as you comment on physics + ml (I check frequently if MuJoCo source code is available at last).

>One of the domains where I hope Julia will excel is precisely as you comment on physics + ml (I check frequently if MuJoCo source code is available at last). Somebody already did a comparison of DifferentialEquations.jl's standard usage against MuJoCo and DiffTaichi and showed that it outperformed them by about 5x-10x. https://arxiv.org/abs/2012.06684 https://arxiv.org/abs/2012.06684 https://homes.cs.washington.edu/~thickstn/ctpg-project-page/ctpg.html https://homes.cs.washington.edu/~thickstn/ctpg-project-page/... That's all showing the raw iteration count to show that it algorithmically is faster, but the time per iteration is also fast for many reasons showcased in the SciMLBenchmarks routinely outperforming C and Fortran solvers (https://github.com/SciML/SciMLBenchmarks.jl https://github.com/SciML/SciMLBenchmarks.jl). So it's excelling pretty well, and things like the automated discovery of black hole dynamics are all done using the universal differential equation framework enabled by the SciML tools (see https://arxiv.org/abs/2102.12695 https://arxiv.org/abs/2102.12695 for that application). What we are missing however is that, right now these simulations are all writing raw differential equations so we do need a better set of modeling tools. That said, MuJoCo and DiffTaichi are not great physical modeling environments for building real systems, instead we would point to Simulink and Modelica as what are really useful for building real-world systems. So it would be cool if there was a modeling language in Julia which extends that universe and directly does optimal code generation for the Julia solvers... and that's what ModelingToolkit.jl is (https://github.com/SciML/ModelingToolkit.jl https://github.com/SciML/ModelingToolkit.jl). That project is still pretty new, but there's already enough to show some large-scale models outperforming Dymola on examples that require symbolic tearing and index reduction, which is far more than what physical simulation environments used for non-scientific purposes (MuJoCo and DiffTaichi) are able to do. See the workshop for details (https://www.youtube.com/watch?v=HEVOgSLBzWA https://www.youtube.com/watch?v=HEVOgSLBzWA). And that's just the top level details, there's a whole Julia Computing product called JuliaSim (https://juliacomputing.com/products/juliasim/ https://juliacomputing.com/products/juliasim/) which is then being built on these pieces to do things like automatically generate ML-accelerated components and add model building GUIs. That said, MuJoCo and DiffTaichi have much better visualizations and animations than MTK. Our focus so far has been on the core routines, making them fast, scalable, stable, and extensive. You'll need to wait for the near future (or build something with Makie) if you want the pretty pictures of the robot to happen automatically. That said, Julia's Makie visualization system has already been shown to be sufficiently powerful for this kind of application (https://nextjournal.com/sdanisch/taking-your-robot-for-a-walk https://nextjournal.com/sdanisch/taking-your-robot-for-a-wal...), so we're excited to see where that will go in the future.