We present a novel deep learning approach to approximate the solution of large, sparse,
symmetric, positive-definite linear systems of equations. These systems arise from many
problems in applied science, eg, in numerical methods for partial differential equations.
Algorithms for approximating the solution to these systems are often the bottleneck in
problems that require their solution, particularly for modern applications that require many
millions of unknowns. Indeed, numerical linear algebra techniques have been investigated …