Machine Learning Optimized Approach for Parameter Selection in MESHFREE
  Simulations
Paulami Banerjee Mohan Padmanabha Chaitanya Sanghavi Isabel Michel Simone Gramsch
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.