Interpolating neural network: A novel unification of machine learning and interpolation theory
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917846750068736 |
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| author | Park, Chanwook Saha, Sourav Guo, Jiachen Zhang, Hantao Xie, Xiaoyu Bessa, Miguel A. Qian, Dong Chen, Wei Wagner, Gregory J. Cao, Jian Liu, Wing Kam |
| author_facet | Park, Chanwook Saha, Sourav Guo, Jiachen Zhang, Hantao Xie, Xiaoyu Bessa, Miguel A. Qian, Dong Chen, Wei Wagner, Gregory J. Cao, Jian Liu, Wing Kam |
| contents | Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, applying this transition in engineering software presents challenges, including low surrogate model accuracy, the curse of dimensionality in inverse design, and rising complexity in physical simulations. We introduce an interpolating neural network (INN), grounded in interpolation theory and tensor decomposition, to realize Engineering Software 2.0 by advancing data training, partial differential equation solving, and parameter calibration. INN offers orders of magnitude fewer trainable/solvable parameters for comparable model accuracy than traditional multi-layer perceptron (MLP) or physics-informed neural networks (PINN). Demonstrated in metal additive manufacturing, INN rapidly constructs an accurate surrogate model of Laser Powder Bed Fusion (L-PBF) heat transfer simulation, achieving sub-10-micrometer resolution for a 10 mm path in under 15 minutes on a single GPU. This makes a transformative step forward across all domains essential to engineering software. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10296 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interpolating neural network: A novel unification of machine learning and interpolation theory Park, Chanwook Saha, Sourav Guo, Jiachen Zhang, Hantao Xie, Xiaoyu Bessa, Miguel A. Qian, Dong Chen, Wei Wagner, Gregory J. Cao, Jian Liu, Wing Kam Machine Learning Artificial Intelligence Neural and Evolutionary Computing Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, applying this transition in engineering software presents challenges, including low surrogate model accuracy, the curse of dimensionality in inverse design, and rising complexity in physical simulations. We introduce an interpolating neural network (INN), grounded in interpolation theory and tensor decomposition, to realize Engineering Software 2.0 by advancing data training, partial differential equation solving, and parameter calibration. INN offers orders of magnitude fewer trainable/solvable parameters for comparable model accuracy than traditional multi-layer perceptron (MLP) or physics-informed neural networks (PINN). Demonstrated in metal additive manufacturing, INN rapidly constructs an accurate surrogate model of Laser Powder Bed Fusion (L-PBF) heat transfer simulation, achieving sub-10-micrometer resolution for a 10 mm path in under 15 minutes on a single GPU. This makes a transformative step forward across all domains essential to engineering software. |
| title | Interpolating neural network: A novel unification of machine learning and interpolation theory |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2404.10296 |