Jonathan Diegelman - Modeling Spacecraft Separation Dynamics in Julia

<<was given at a SIAM conference and the recording can be found here: ▶ this video >> At 14:00, the interactive slider exploration due to the speed is amazing. Over 500 Monte Carlo simulations in less than 60ms.

You should check out this case study of migrating Simulink to Julia at NASA: ▶ this video From Simulink to Julia, his code went from 1000 lines to 50 lines and ran 15,000x faster.

> Definitely 15000 times faster than MATLAB as claimed by JuliaComputing can be achieved with such seemingly concrete and realistic benchmarks. I believe you're talking about NASA Launch Services engineers claiming Julia's ModelingToolkit simulations outperformed Simulink by 15,000x? That claim was of course not made by Julia Computing or anyone affiliated by Julia Computing, which is pretty clear because the person who makes the claim very clearly describes his affiliation at the beginning of the video. The source is here: ▶ this video , at 12:55. You did watch the whole video to understand the application and the caveats etc. instead of just reading the headline and immediately coming to a conclusion, right?

I would be cautious saying Simulink has no Julia equivalent. This NASA engineer switched from Simulink to SciML and obtained a 15,000x speed up while his code went from 1000 lines to 50 lines long: ▶ this video

ModelingToolkit.jl, a comparison is given here: link . A showcase from one of the library's users, demonstrating a 15,000x acceleration, was given at a SIAM conference and the recording can be found here: ▶ this video

Lots to say here. First of all, the community growth has been pretty tremendous and I couldn't really ask for more. We're seeing tens of thousands of visitors to the documentation of various packages, and have some high profile users. For example, NASA showing a 15,000x acceleration ( ▶ this video ) and the Head of Clinical Pharmacology at Moderna saying SciML-based Pumas "has emerged as our 'go-to' tool for most of our analyses in recent months" in 2020 (see link ). We try to keep a showcase ( link ) but at this point it's hard to stay on top of the growth. I think anyone would be excited to see an open source side project reach that level of use. Since we tend to focus on core numerical issues (stiffness) and performance, we target the more "hardcore" people in scientific disciplines who really need these aspects and those communities are the ones seeing the most adoption (pharmacology, systems biology, combustion modeling, etc.). Indeed the undergrad classes using a non-stiff ODE solver on small ODEs or training a neural ODE on MNIST don't really have those issues so they aren't our major growth areas. That's okay and that's probably the last group that would move. In terms of the developer team, throughout the SciML organization repositories we have had around 30 people who have had over 100 commits, which is similar in number to NumPy and SciPy. Julia naturally has a much lower barrier to entry in terms of committing to such packages (since the packages are all in Julia r

NASA switched from SIMULINK to ModelingToolkit (Julia) for modeling spacecraft dynamics [0]. They found that the latter was much easier and gave a 15,000x (!!!) performance improvement. [0]: ▶ this video

Sure! NASA: ▶ this video Moderna: link link There are many many more. These unique and sought after capability are what got Julia Computing its 24 mil series A ( link )

For satellite seperation dynamics modeling, NASA switched from MATLAB (Simulink) to Julia and got a 15,000x performance boost. ▶ this video