
Differentialequations.jl v6.9.0: Multi-GPU Implicit Ode Solves, SciPy/R Bindings - ChrisRackauckas
https://juliadiffeq.org/2019/12/03/MultiGPU.html
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ViralBShah
Chris's ODE solver suite is a huge improvement over decades old Fortran
libraries. It is also really pushing the boundary on what is possible to do
with GPUs beyond the standard neural network workloads.

This release provides capabilities that allow for significant improvements in
the capabilities of simulations in pharma, engineering and physics-informed
machine learning.

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vsskanth
I'm guessing you still have to describe the differential equation structure in
Julia ? Can it handle black box functions that provide the state and
derivatives (like an FMU in model exchange) ?

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ChrisRackauckas
Yes, there really isn't a good way to do a black box binary. We will be
setting things up with eFMU though which has the equations, and that we can
generate GPU code for through ModelingToolkit quite easily. We will need to
get that setup as a standard feature when eFMU is standardized

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vsskanth
Thanks. I'll check out what eFMU is.

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ChrisRackauckas
It's the planned extensions to the FMU:
[https://www.researchgate.net/publication/336775272_Standardi...](https://www.researchgate.net/publication/336775272_Standardizing_eFMI_for_embedded_systems_with_physical_models_in_the_production_code_software)

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freemint
I found this package to be highly useful. Great to see that it is progressing
like this. Still waiting for fractional derivatives.

