Hello everyone! I am trying to play with solving PDEs using neural networks. As I understand, this area is an example of self-supervised learning when the model (neural network itself) automatically finds its derivatives with respect to input variables, inserts them into the PDE given and then atte…

I have tried for a couple hours, but I have not found any solution. You could export the model and use mxnet python to do the training. Perhaps, you could still use the Python integration to send the equations from Wolfram language to Python.

That post involves turning the solution into a supervised learning problem by building a dataset of finalized renders of the pde solution generated by NDSolve, then training a network on the dataset. I am not exactly sure how that method ideally compares to something like the techniques described h…

Also, it seems MXNet has had a high-priority ticket to support second-derivative computations for several years now, but it does not appear any progress has been made. Perhaps this is not even possible with the MXNet backend Wolfram is using. MXNet Jira MXNet GitHub issue Theoretically, the…

Thank you all for your replies! Yes, my question arose exactly from Python - my colleague works on the ANN-based eikonal equation solver implemented with Tensorflow - and I wanted to play with this problem using WL. A particular thanks to Sam’s reply: interesting approach to the problem!

Solving PDEs using neural networks?

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samcarrettie (Sam Carrettie) July 1, 2021, 8:20pm 3

Not sure if this is useful to you guys: Teaching neural networks to solve partial differential equations [WSS18] Teaching neural networks to solve partial differential equations

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Solving PDEs using neural networks?

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