Hi Marius. Yes, it's easy to do that as long as you know basic usage of PyTorch. For saving and loading model, here's a useful link. In the context of neurodiffeq, you want to perform the following procedures:

1. Make sure you have the model class MyNetwork saved in some file model.py. Note that MyNetwork must be a subclass of torch.nn.Module
2. Create one or more (depending on the number of functions you are solving for) MyNetwork instances:

my_nets = [MyNetwork(...), MyNetwork(...), ...]

3. Instantiate your solver and pass your model(s)

solver = Solver1D(
    ...
    nets=my_nets,
)

4. Do the training and get your networks. Currently, neurodiffeq doesn't make a copy of the networks passed to it, so solver.nets is the same object as my_nets created earlier

solver.fit(max_epochs=xxx, ...)
my_nets = solver.nets # you can skip this step if you still have access to `my_nets` created earlier

5. Save your model

torch.save({f'net_{i}': net.state_dict() for i, net in enumerate(nets)}, YOUR_MODEL_PATH)

6. In another script, instantiate your model using exactly the same architecture and load the weights

loaded_nets = [MyNetwork(...), MyNetwork(...), ...]
checkpoint = torch.load(YOUR_MODEL_PATH)
for i, net in enumerate(loaded_nets):
    net.load_state_dict(checkpoint[f'net_{i}'])

7. Redo step 3~4, but change my_nets to loaded_nets

That sounds great! And it is possible to use both Adam optimiser and L-BFGS/L-BFGS-B, right?

Most optimizers are currently supported, except LBFGS, which is a little tricky (see #83). Luckily, we seem to have a solution proposed just now. Yet, we still need to run the tests.

I'm not familiar with L-BFGS-B, but it appears that this optimizer has not been implemented in PyTorch (see here). So currently, you can't use L-BFGS-B without implementing it yourself.