Optimization of DEM parameters using multi-objective reinforcement learning

Simulations with the Discrete Element Method (DEM) have become prominent for analyzing bulk behavior in various industries. For each application the material has to be analyzed while the material parameters have to be determined to ensure a valid and reliable result. However, material properties available in the literature are hardly usable and unsuitable for a macroscopic analysis of the bulk behavior. Thus, the material has to be tested and evaluated to calibrate it with suitable DEM material parameters. In this work, a novel approach for DEM calibration with a parameter optimization based on multi-objective reinforcement learning is proposed. This approach uses the results of two different environments and trains an agent to find a suitable material parameter-set with a low number of required iterations and a small number of hyper-parameters. To ensure the applicability of the developed approach, three materials with different characteristics are calibrated and validated. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Simulations with DEM require determining suitable material parameters to ensure validity and reliability of the results, a novel approach based on multi-objective reinforcement learning for material parameter optimization is proposed.