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
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:
@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