  This paper presents a novel neural network architecture for the purpose of
pervasive visualisation of a 3D human upper limb musculoskeletal system model.
Bringing simulation capabilities to resource-poor systems like mobile devices
is of growing interest across many research fields, to widen applicability of
methods and results. Until recently, this goal was thought to be out of reach
for realistic continuum-mechanical simulations of musculoskeletal systems, due
to prohibitive computational cost. Within this work we use a sparse grid
surrogate to capture the surface deformation of the m.~biceps brachii in order
to train a deep learning model, used for real-time visualisation of the same
muscle. Both these surrogate models take 5 muscle activation levels as input
and output Cartesian coordinate vectors for each mesh node on the muscle's
surface. Thus, the neural network architecture features a significantly lower
input than output dimension. 5 muscle activation levels were sufficient to
achieve an average error of 0.97 +/- 0.16 mm, or 0.57 +/- 0.10 % for the 2809
mesh node positions of the biceps. The model achieved evaluation times of 9.88
ms per predicted deformation state on CPU only and 3.48 ms with GPU-support,
leading to theoretical frame rates of 101 fps and 287 fps respectively. Deep
learning surrogates thus provide a way to make continuum-mechanical simulations
accessible for visual real-time applications.
