Lauri Salmela

Camera algorithm engineer at Huawei Technologies. Photonics & machine learning

[TAU Photonics @tau_photonics]

Great work has been released by Lauri Salmela @salmelala from the Ultrafast Photonics group @GGoery @flagshipprein
💡
The work has been also selected as an Editor's Pick by @OpticaWorldwide highlighting papers with excellent quality 💥

🔍 Check it out: https://t.co/W2V9kHNre4

[Lauri Salmela @salmelala]

@johnmdudley I have done some comparison between Python and Matlab only, and got worse results with Python at least in my case. But GNLSE solver in Fortran could be neat indeed

[Lauri Salmela @salmelala]

@johnmdudley @GGoery @dbrunner_lab I got 10/10 :)

[Lauri Salmela @salmelala]

@the_atman @GGoery @johnmdudley Hi Alexander. I have not really considered the interpretability of RNNs, and personally I don't see the opacity of RNNs as a problem at least with our research. Related to the preprint, I like the idea of hybrid models. Maybe this is something we could look into in the future

[Lauri Salmela @salmelala]

@omairg @PhotonicsMeetup Hi. The fiber length could be changed freely here. Only issue may come from spectral or temporal broadering higher than in the samples used in the training if the length is increased too much. We only tested sech-typed input pulses but this approach it's limited to them only

[Lauri Salmela @salmelala]

We show how a recurrent neural network can predict the propagation dynamics of ultrashort pulses in an optical fiber for supercontinuum generation @PhotonicsMeetup #POM20ju #POM20AI

[Lauri Salmela @salmelala]

@Ariel_Levenson @PhotonicsMeetup Hi Ariel. For a single realisation, the gain in time depends on the simulation parameters (number of grid points) but is in our case around factor of 3-5 times faster. However, if you have multiple input conditions, the speedup is in the range of 20-50 times faster.

[Lauri Salmela @salmelala]

@FlaviaTimpu @PhotonicsMeetup Hi Flavia, thanks for the question. We have here tested only one fiber (single-mode silica PCF), so the dispersion and the nonlinearity are always the same. In the training and testing samples, we have varied the input pulse peak power between 500W and 2kW with 100fs duration.

[Lauri Salmela @salmelala]

@sylvaingigan @PhotonicsMeetup Hi Sylvain. So far we have only tested networks with LSTM cells. Reservoir computing or other backpropagation algorithms (e.g. GRU cells) could be used as well. The choice between these options depends on the user's expertise.