LaDEEP: A Deep Learning-based Surrogate Model for Large Deformations of Elastic-Plastic Solids
Keywords: Continuum Mechanics, Large Deformation, Elastic-Plastic Solid, Transformer, Deep Learning-based Surrogate Model
TL;DR: We propose LaDEEP, a deep learning-based surrogate model for large deformations of elastic-plastic solids.
Abstract: The scientific computing for large deformations of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximations, which are constrained by an inherent trade-off between accuracy and efficiency. Recently, Deep Learning models have achieved impressive progress in solving PDEs. 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}formations of \textbf{E}lastic-\textbf{P}lastic Solids. We encode the partitioned regions of the involved 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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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 7221
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