Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations
This work enhances accessibility and usability of meshfree simulations for a broader user base in scientific and engineering applications, though it appears incremental as it applies existing ML techniques to a specific software tool.
The researchers tackled the challenge of manually determining optimal parameter combinations in MESHFREE simulations, which is difficult especially for less experienced users, by introducing a novel ML-optimized approach using active learning and regression trees that demonstrates the impact of input combinations on results quality and computation time.
Meshfree simulation methods are emerging as compelling alternatives to conventional mesh-based approaches, particularly in the fields of Computational Fluid Dynamics (CFD) and continuum mechanics. In this publication, we provide a comprehensive overview of our research combining Machine Learning (ML) and Fraunhofer's MESHFREE software (www.meshfree.eu), a powerful tool utilizing a numerical point cloud in a Generalized Finite Difference Method (GFDM). This tool enables the effective handling of complex flow domains, moving geometries, and free surfaces, while allowing users to finely tune local refinement and quality parameters for an optimal balance between computation time and results accuracy. However, manually determining the optimal parameter combination poses challenges, especially for less experienced users. We introduce a novel ML-optimized approach, using active learning, regression trees, and visualization on MESHFREE simulation data, demonstrating the impact of input combinations on results quality and computation time. This research contributes valuable insights into parameter optimization in meshfree simulations, enhancing accessibility and usability for a broader user base in scientific and engineering applications.