Acceleration of Radiation Transport Solves Using Artificial Neural Networks
This accelerates radiation transport solvers for computational physics applications, but is incremental as it replaces a specific step with a machine learning approach.
The paper tackles the computational cost of Gaussian Elimination in Discontinuous Finite Element Methods for radiation transport by replacing it with a shallow Artificial Neural Network, achieving at least a 4x speed-up with 1-3% mean absolute error.
Discontinuous Finite Element Methods (DFEM) have been widely used for solving $S_n$ radiation transport problems in participative and non-participative media. In the DFEM $S_n$ methodology, the transport equation is discretized into a set of algebraic equations that have to be solved for each spatial cell and angular direction, strictly preserving the following of radiation in the system. At the core of a DFEM solver a small matrix-vector system (of 8 independent equations for tri-linear DFEM in 3D hexehdral cells) has to be assembled and solved for each cell, angle, energy group, and time step. These systems are generally solved by direct Gaussian Elimination. The computational cost of the Gaussian Elimination, repeated for each phase-space cell, amounts to a large fraction to the total compute time. Here, we have designed a Machine Learning algorithm based in a shallow Artificial Neural Networks (ANNs) to replace that Gaussian Elimination step, enabling a sizeable speed up in the solution process. The key idea is to train an ANN with a large set of solutions of random one-cell transport problems and then to use the trained ANN to replace Gaussian Elimination large scale transport solvers. It has been observed that ANNs decrease the solution times by at least a factor of 4, while introducing mean absolute errors between 1-3 \% in large scale transport solutions.