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Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations
March 21, 2024, 4:42 a.m. | Paulami Banerjee, Mohan Padmanabha, Chaitanya Sanghavi, Isabel Michel, Simone Gramsch
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv cfd computational cs.lg dynamics fields fluid dynamics machine machine learning mesh overview physics.flu-dyn publication research simulation simulations software type
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