LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids

• URL: http://arxiv.org/abs/2506.06001v1
• Date: Fri, 06 Jun 2025 11:47:37 GMT
• Title: LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
• Authors: Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu,
• Abstract summary: We introduce LaDEEP, a deep learning-based surrogate model for textbfLarge textbfDeformation of textbfElastic-textbfPlastic Solids.<n>To characterize the physical process of the solid deformation, a two-stage Transformer-based module is designed to predict the deformation with the sequence of tokens as input.<n>LaDEEP achieves five magnitudes faster speed than finite element methods with a comparable accuracy, and gains 20.47% relative improvement on average compared to other deep learning baselines.
• Score: 21.697159152687288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientific computing for large deformation of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximation and are constrained by an inherent trade-off between accuracy and efficiency. Recently, deep learning models have achieved impressive progress in solving the continuum mechanism. While previous models have explored various architectures and constructed coefficient-solution mappings, they are designed for general instances without considering specific problem properties and hard to accurately handle with complex elastic-plastic solids involving contact, loading and unloading. In this work, we take stretch bending, a popular metal fabrication technique, as our case study and introduce LaDEEP, a deep learning-based surrogate model for \textbf{La}rge \textbf{De}formation of \textbf{E}lastic-\textbf{P}lastic Solids. We encode the partitioned regions of the involved slender solids into a token sequence to maintain their essential order property. To characterize the physical process of the solid deformation, a two-stage Transformer-based module is designed to predict the deformation with the sequence of tokens as input. Empirically, LaDEEP achieves five magnitudes faster speed than finite element methods with a comparable accuracy, and gains 20.47\% relative improvement on average compared to other deep learning baselines. We have also deployed our model into a real-world industrial production system, and it has shown remarkable performance in both accuracy and efficiency.

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