Low Storage Methods

Hi,

I've been working on low-storage Runge-Kutta methods recently, in particular Williamson 2N methods.

These methods compute the ODE flow via a recursive scheme that only uses two registers, so they can be useful in memory-constrained settings like PDE solves. The tradeoff is usually that they need more stages for a given order.

I put together an implementation here: diffrax-lowstorage.

At the moment it includes:

• a 2N solver class with substantially lower memory use at the same order,
• embedded error estimates,
• and two well-known methods.

@patrick-kidger, would there be any interest in merging something like this into Diffrax? I'd be happy to open a PR and make whatever changes you think would be needed.

As far as I know, the only other mainstream library with this kind of support is Diffeq.jl LowStorageRK, so it could be a nice addition.

Thanks

Review the linked diffrax-lowstorage implementation and compare its 2N solver, embedded error estimates, and two methods with Diffrax. The issue does not name repository files or tests; done would require an agreed integration scope and a contribution path.