  Increased demands for high-performance materials have led to advanced
composite materials with complex hierarchical designs. However, designing a
tailored material microstructure with targeted properties and performance is
extremely challenging due to the innumerable design combinations and
prohibitive computational costs for physics-based solvers. In this study, we
employ a neural operator-based framework, namely Fourier neural operator (FNO)
to learn the mechanical response of 2D composites. We show that the FNO
exhibits high-fidelity predictions of the complete stress and strain tensor
fields for geometrically complex composite microstructures with very few
training data and purely based on the microstructure. The model also exhibits
zero-shot generalization on unseen arbitrary geometries with high accuracy.
Furthermore, the model exhibits zero-shot super-resolution capabilities by
predicting high-resolution stress and strain fields directly from
low-resolution input configurations. Finally, the model also provides
high-accuracy predictions of equivalent measures for stress-strain fields
allowing realistic upscaling of the results.
