  Engineering simulations for analysis of structural and fluid systems require
information of contacts between various 3-D surfaces of the geometry to
accurately model the physics between them. In machine learning applications,
3-D surfaces are most suitably represented with point clouds or meshes and
learning representations of interacting geometries form point-based
representations is challenging. The objective of this work is to introduce a
machine learning algorithm, ActivationNet, that can learn from point clouds or
meshes of interacting 3-D surfaces and predict the quality of contact between
these surfaces. The ActivationNet generates activation states from point-based
representation of surfaces using a multi-dimensional binning approach. The
activation states are further used to contact quality between surfaces using
deep neural networks. The performance of our model is demonstrated using
several experiments, including tests on interacting surfaces extracted from
engineering geometries. In all the experiments presented in this paper, the
contact quality predictions of ActivationNet agree well with the expectations.
