Structure-Guided Gauss-Network Method: Linear Advection-Reaction Equation

The least-squares neural network (LSNN) method introduced in [5] for linear advection-reaction equations is capable of accurately approximating discontinuous solutions without a priori knowledge of the interface location.

However, the resulting discretization is a non-convex optimization problem that is computationally intensive and complex.

In this paper, we propose a structure-guided Gauss-Newton (SgGN) method that alternates between the linear (output) and the nonlinear (hidden layer) parameters.

At each outer iteration, the linear parameters are computed by a linear solver, and the nonlinear parameters are updated by a modified Gauss-Newton (GN) method that explicitly removes the singularities of the GN matrix.

Numerical experiments for all test problems presented in [5] show that the SgGN method is superior to the Adam optimizer [13], the commonly used first-order optimization algorithm, not only in computational cost but, more importantly, in accuracy