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.

翻译：无网格仿真方法正逐渐成为传统网格方法的强有力替代方案，尤其在计算流体力学和连续介质力学领域。本文全面概述了将机器学习与弗劳恩霍夫MESHFREE软件（www.meshfree.eu）相结合的研究工作。该软件是一款基于广义有限差分法数值点云的强大工具，可有效处理复杂流动域、运动几何体和自由表面，同时允许用户精细调整局部加密和质量参数，以在计算时间与结果精度之间实现最优平衡。然而，手动确定最佳参数组合具有挑战性，尤其对经验不足的用户而言。我们提出了一种新颖的基于机器学习的优化方法，该方法采用主动学习、回归树和可视化技术，基于MESHFREE仿真数据展示了输入参数组合对结果质量和计算时间的影响。本研究为无网格仿真中的参数优化提供了重要见解，增强了科学和工程应用中更广泛用户群体的可访问性与易用性。