AI-Based Surrogate Modeling for Highly Efficient Soil-Tool Simulation

The Discrete Element Method (DEM) is broadly used for soil modeling, especially if a realistic prediction of interaction forces with solid materials, i.e. tools is required. While recent enhancements of computing power allow for faster computing times in many fields, DEM-based calculations are still far from real-time, typically by a factor of 100 or more. This is a bottle neck within the design and development processes of agricultural and construction machinery, which are relying on the interaction forces with soils. Finding a decent surrogate model, combining higher computing speeds without loosing accuracy in the prediction of soil-tool interaction forces, would be highly beneficial. Here, we discuss an approach based on recurrent neural networks with the potential of combining real-time capability with accurate soil-tool interaction force prediction.