Applied Machine Learning for Surrogate Modeling: A Spatio-Temporal Approach

A recent trend in machine learning (ML) is its application to surrogate modeling for computationally intractable simulations. The general approach is to train an ML model using data obtained from the field or extensive results from prior simulations. Although training can be an expensive process, it is typically a one-time requirement and the result can be used to reduce the time otherwise required to calculate the solution. In this work, we train and apply a sophisticated ML model that has both spatial and temporal capabilities to aid in solving a well-known two-dimensional heat transfer finite difference problem which serves as a proxy application for our external funding sponsor, Los Alamos National Laboratory (LANL). Additionally, we train and apply the same model to a 2D boiling water simulation in order to contrast the model's generality in two applications with different levels of mathematical complexity and present the obtained predictive results.