Predicting transient response using data-driven models for ball-impact simulations
This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. We focus on the canonical problem of ball impact on laminated structures, which captures essential physics while maintaining computational tractabilit...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
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| Series: | Journal of Physics Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2399-6528/ad9f1f |
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| Summary: | This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. We focus on the canonical problem of ball impact on laminated structures, which captures essential physics while maintaining computational tractability. Novel contributions include: (1) development of a temporal multi-resolution strategy for stable long-time predictions, (2) systematic comparison of U-Nets and Fourier Neural Operators as spatial ML kernels, and (3) demonstration of accurate non-local metric predictions across full time-horizons. Using a synthetic dataset of 6500 impact scenarios, we achieve 3.5–8% prediction accuracy while providing 10,000x speedup compared to traditional FEM simulations. The proposed methodology enables rapid virtual prototyping for impact-resistant design optimization. |
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| ISSN: | 2399-6528 |