We propose a new global solution algorithm for continuous time heterogeneous agent economies with aggregate shocks. First, we approximate the state space so that equilibrium in the economy can be characterized by one high, but finite, dimensional partial differential equation. Second, we approximate the value function using neural networks and solve the differential equation using deep learning tools. We refer to the solution as an Economic Model Informed Neural Network (EMINN). The main advantage of this technique is that it allows us to find global solutions to high dimensional, non-linear problems. We demonstrate our algorithm by solving two canonical models in the macroeconomics literature: the Aiyagari (1994) model and the Krusell and Smith (1998) model.