SciML

DifferentialEquations.jl v6.4.0: Full GPU ODE, Performance, ModelingToolkit

This is a huge release. We should take the time to thank every contributor to the JuliaDiffEq package ecosystem. A lot of this release focuses on performance features. The ability to use stiff ODE solvers on the GPU, with automated tooling for matrix-free Newton-Krylov, faster broadcast, better Jacobian re-use algorithms, memory use reduction, etc. All of these combined give some pretty massive performance boosts in the area of medium to large sized highly stiff ODE systems. In addition, numerous robustness fixes have enhanced the usability of these tools, along with a few new features like an implementation of extrapolation for...

DifferentialEquations.jl 6.0: Radau5, Hyperbolic PDEs, Dependency Reductions

This marks the release of DifferentialEquations.jl v6.0.0. Here’s a low down of what has happened in the timeframe.

DifferentialEquations.jl 5.0: v1.0, Jacobian Types, EPIRK

This marks the release of DifferentialEquations.jl. There will be an accompanying summary blog post which goes into more detail about our current state and sets the focus for the organization’s v6.0 release. However, for now I would like to describe some of the large-scale changes which have been included in this release. Much thanks goes to the Google Summer of Code students who heavily contributed to these advances.

DifferentialEquations.jl 4.6: Global Sensitivity Analysis, Variable Order Adams

Tons of improvements due to Google Summer of Code. Here’s what’s happened.

DifferentialEquations.jl 4.5: ABC, Adaptive Multistep, Maximum A Posteriori

Once again we stayed true to form and didn’t solve the problems in the development list but adding a ton of new features anyways. Now that Google Summer of Code (GSoC) is in full force, a lot of these updates are due to our very awesome and productive students. Here’s what we got.

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