Floris Laporte

I like the idea of calling them connection models. Sounds good. I'll let you take on this issue. Let me know if you need any help.

Hi @tvt173 , this method is indeed the default for PyTorch and is generally speaking a pretty good approach. The problem is that JAX functions are supposed to be [pure...

Hi Alex, I'll try to take a look today or tomorrow.

I have come across the `pytorch_sparse` library indeed. However, I have never used it. If it is indeed the de-facto sparse library for PyTorch, it might indeed be a good...

Thanks Manuel! Representing the system as a block diagonal matrix might indeed be a smart way of speeding up the operation. However, I'm very busy at the moment and I'm...

probably the easiest way to do this is to use `sax.evaluate_circuit` in stead of circuit, which accepts the calculated s-matrices as instances (rather than the model functions). This allows you...

I meant `sax.backends.evaluate_circuit`, which can be used in conjunction with `sax.backends.analyze_circuit` if you want more control over how everything gets executed. Note that this assumes a 'flat' netlist... Parallelizing the...

alternatively you could probably use the dependency dag and the models in the CircuitInfo (second item returned by sax.circuit) to execute each dependency in the right order. This might take...

Hi Tyler! That's amazing! I'll take a look at the notebook today. Now that I have an example to work from I can maybe patch SAX in a way that...

Hi @uranium11010 , Sparse solvers are tricky. There are lots of sparse solvers out there (see for example SuiteSparse). Each of them have different use cases and are ideal for...