[physicsguy]

I've tried both against similar and haven't found it such a clear cut difference. I still find neither are able to fully implement a complex algorithm I worked on in the past correctly with the same inputs. Not sharing exactly the benchmark I'm using but think about something for improving performance of N^2 operations that are common in physics and you can probably guess the train of thought.

[keldaris]

I've had reasonable success using GPT for both neighbor list and Barnes-Hut implementations (also quad/oct-trees more generally), both of which fit your description, haven't tried Ewald summation or PME / P3M. However, when I say "reasonable success", I don't mean "single shot this algo with a minimal prompt", only that the model can produce working and decently optimized implementations with fairly precise guidance from an experienced user (or a reference paper sometimes) much faster than I would write them by hand. I expect a good PME implementation from scratch would make for a pretty decent benchmark.

[physicsguy]

Think another level of complexity of algorithm, different expansion bases plus a mix of input sources. Also not trying to one-shot it.

[tirutiru]

I can roughly guess the train of thought and I am a bit surprised that Claude is failing you.

That said, I am puzzled at the algorithms that Claude & GPT "get" and ones that they do not.

(former physicist here. would love to know the kind of things you're working on. email on my profile)