  In insurance mathematics optimal control problems over an infinite time
horizon arise when computing risk measures. Their solutions correspond to
solutions of deterministic semilinear (degenerate) elliptic partial
differential equations. In this paper we propose a deep neural network
algorithm for solving such partial differential equations in high dimensions.
The algorithm is based on the correspondence of elliptic partial differential
equations to backward stochastic differential equations with random terminal
time.
