Abstract: In this work we present our results designing a deep neural network (DNN) to act as a surrogate model for costly HPC simulations. In order to determine a ground vehicle's gap crossing ability in extreme weather scenarios, several HPC simulations are currently used. Hydrologic models are first run to determine the environmental conditions over an area of interest. Once these conditions are known they are given, along with the terrain data, to a vehicle simulation which determines if a particular vehicle can cross a stream at a given point. Every point of interest must be evaluated independently, which quickly becomes infeasible for a large numbers of crossing points. In order to accelerate this phase of the process, we have created a DNN that acts as a surrogate model for the vehicle simulator. Despite several challenges converting irregular data into a form that can be used with a DNN, and incorporating scalars into the models, we were able to produce DNN models that predicted the gap crossing ability of all vehicle types with over 95% accuracy.