  Deep learning surrogate models are being increasingly used in accelerating
scientific simulations as a replacement for costly conventional numerical
techniques. However, their use remains a significant challenge when dealing
with real-world complex examples. In this work, we demonstrate three types of
neural network architectures for efficient learning of highly non-linear
deformations of solid bodies. The first two architectures are based on the
recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have
shown promising performance for learning on mesh-based data. The third
architecture is Perceiver IO, a very recent architecture that belongs to the
family of attention-based neural networks--a class that has revolutionised
diverse engineering fields and is still unexplored in computational mechanics.
We study and compare the performance of all three networks on two benchmark
examples, and show their capabilities to accurately predict the non-linear
mechanical responses of soft bodies.
