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SymPolyNN

This repository contains the code used to generate the results presented in our paper, Discovering Interpretable Elastoplasticity Models via the Neural Polynomial Method Enabled Symbolic Regressions published in the Computer Methods in Applied Mechanics and Engineering (CMAME) journal.

Note

The code is provided as-is for academic use only. Please ensure you cite our paper if you use this code in your research.

TODO

A README guidance will be prepared soon.

Some examples will be added/updated.

Some coding parts in FEM and stress integration need to be modularized.

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A fully interpretable method based on the polynomial regression in the feature space build by neural neural network and symbolic regression

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