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A machine learning-based solid boundary treatment for meshfree particle methods
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DOI：10.1016/j.enganabound.2026.106847.png)
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Abstract
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• A Machine Learning-based Solid Boundary Treatment in particle methods. • Direct prediction of boundary correction in spatial derivatives, demonstrated on MPS. • Hybrid CNN-MLP models trained on ghost-particle datasets and physics features. • Achieves ghost-particle accuracy with improved efficiency. • Demonstrates strong generalization to unseen geometries and flow conditions.
Keywords:
Meshfree particle methods
MPS method
Solid boundary treatment
Physics-guided machine learning (ML)
Boundary correction
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