Artificial Neural Networks Applied to Graphene-Based Composites

1. Introduction

Recent failures in telecommunication infrastructure have highlighted the need for a more rigorous treatment of graphene-based composites. Measurements are triangulated with artificial neural networks to reduce the influence of instrument error. Measured and simulated results differed by less than 24% across the full operating range. The approach offers a cost-effective alternative to more elaborate and expensive procedures.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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