What’s a better way to ring in the new year than to announce new features? This ecosystem 3.3 release we have a few exciting developments, and at the top of the list is new IMEX schemes. Let’s get right to it.
DifferentialEquations.jl 3.2 is just a nice feature update. This hits a few long requested features.
The DifferentialEquations.jl 3.0 release had most of the big features and was featured in a separate blog post. Now in this release we had a few big incremental developments. We expanded the capabilities of our wrapped libraries and completed one of the most requested features: passing Jacobians into the IDA and DASKR DAE solvers. Let’s just get started there:
The end of the summer cycle means that many things, including Google Summer of Code projects, are being released. A large part of the current focus has been to develop tools to make solving PDEs easier, and also creating efficient tools for generalized stiff differential equations. I think we can claim to be one of the first libraries to include methods for stiff SDEs, one of the first for stiff DDEs, and one of the first to include higher order adaptive Runge-Kutta Nystrom schemes. And that’s not even looking at a lot of the more unique stuff in this release. Take a look.
This has been a very productive summer! Let me start by saying that a relative newcomer to the JuliaDiffEq team, David Widmann, has been doing some impressive work that has really expanded the internal capabilities of the ordinary and delay differential equation solvers. Much of the code has been streamlined due to his efforts which has helped increase our productivity, along with helping us identify and solve potential areas of floating point inaccuracies. In addition, in this release we are starting to roll out some of the results of the Google Summer of Code projects. Together, there’s some really exciting stuff!