  Current trends in the computer graphics community propose leveraging the
massive parallel computational power of GPUs to accelerate physically based
simulations. Collision detection and solving is a fundamental part of this
process. It is also the most significant bottleneck on physically based
simulations and it easily becomes intractable as the number of vertices in the
scene increases. Brute force approaches carry a quadratic growth in both
computational time and memory footprint. While their parallelization is trivial
in GPUs, their complexity discourages from using such approaches. Acceleration
structures -- such as BVH -- are often applied to increase performance,
achieving logarithmic computational times for individual point queries.
Nonetheless, their memory footprint also grows rapidly and their
parallelization in a GPU is problematic due to their branching nature. We
propose using implicit surface representations learnt through deep learning for
collision handling in physically based simulations. Our proposed architecture
has a complexity of O(n) -- or O(1) for a single point query -- and has no
parallelization issues. We will show how this permits accurate and efficient
collision handling in physically based simulations, more specifically, for
cloth. In our experiments, we query up to 1M points in 300 milliseconds.
