Julia is a big winner in the ARPA E dIFFERENTIATE program. ARPA-E just announced that will fund 23 Projects to Accelerate the Energy Technology Design Process. Three of the projects will use Julia to build computational platforms. Two of the projects (including ours at LANL) will use machine learning based on differentiable programming in Julia.
It’s not clear from the descriptions which of the proposals will use Julia (except for the proposal from Julia Computing, of course). Could you list the 5 projects that are using Julia in this program?
On a related note, we also have a geothermal extraction project which also focuses on machine learning. Again we use Julia. So we cover now all the energy solutions
Nice to see that the “Differentiable Programming in Julia” infrastructure is getting good funding.
I actually got a (much smaller!) funding for a 6 months Master internship on applying Julia+AD to microgrid sizing (~about the class of problem that the NREL spinoff program HOMER Pro is addressing).
If a Master student wants to visit France this Spring, please contact me!
Yes, this is already implemented. Just use Juila to run parallel calculations on a reasonably powerful server (preferably using all cores) for a couple of days.
If, for some reason, your calculations are too fast (which, unfortunately, is a drawback Julia users frequently encounter), you can always ask the community for extra workload — just provide login details.
Who thought of this acronym? Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements (DIFFERENTIATE). Totally impactful