Radar systems engineer

Saab Surveillance

Removing computational bottlenecks with deep learning

Deep learning has transformed the fields of computer vision and natural language processing. There is a more recent trend in using neural networks to solve differential equations. It is less well known and has so far mostly gained academic interest. Computer aided engineering is, under the hood, based on solving partial differential equations (PDE) approximately with classical methods such as the finite element method. A crash simulation takes hours to run on a computer cluster and the PDE has 4 dimensions (three space dimensions and one time dimension). Last year, various nonlinear PDE with 10000 space variables were solved approximately with deep learning on much smaller computers. The problems are not perfectly comparable but this result still indicates the ability of deep learning to scale beyond classical methods. Both automatic control and sensor fusion problems can be formulated and solved in terms of PDE, but due to computational complexity of classical methods these are not used in practical algorithms. In this talk I discuss how such equations for stochastic control and potentially for sensor fusion can be used for deriving practical algorithms, with deep learning being the enabler.

Adam Andersson works as radar systems engineer at Saab Surveillance. He is a mathematician with research background in numerical analysis and probability theory. During his four years in industry he has been working with applications of machine learning and sensor fusion, as well as more research oriented machine learning projects. His role has a semi-academic character with collaborations, master and PhD supervision at Chalmers.