用多任务神经网络实现弹性塑性变形的实时高精度模拟
Towards a Real-Time Simulation of Elastoplastic Deformation Using Multi-Task Neural Networks
- 结合降维与长短期记忆网络，通过多任务学习共享参数提升泛化能力
- 平均绝对误差低于0.40%，仅需20样本即可快速适配新变量
- 计算速度比传统有限元法快百万倍，适合工程实时预测场景
本研究提出一种融合本征正交分解、长短期记忆网络与多任务学习的代理建模框架，可实现实时高精度预测弹性塑性变形。相比单任务神经网络，该方法在多种状态变量上均实现低于0.40%的平均绝对误差，且通过共享层有效缓解过拟合，增强泛化能力。在实际应用中，预训练的多任务模型仅需20个样本即可高效训练新增变量，显著优于通常需约100样本的单任务模型。计算速度较传统有限元分析提升约一百万倍，极大推进了工程实时预测建模的发展。尽管仍需在更复杂模型上验证，该框架在效率与精度方面展现出显著潜力，尤其适用于实时应用场景。
原文摘要 · Abstract (English)
This study introduces a surrogate modeling framework merging proper orthogonal decomposition, long short-term memory networks, and multi-task learning, to accurately predict elastoplastic deformations in real-time. Superior to single-task neural networks, this approach achieves a mean absolute error below 0.40\% across various state variables, with the multi-task model showing enhanced generalization by mitigating overfitting through shared layers. Moreover, in our use cases, a pre-trained multi-task model can effectively train additional variables with as few as 20 samples, demonstrating its deep understanding of complex scenarios. This is notably efficient compared to single-task models, which typically require around 100 samples. Significantly faster than traditional finite element analysis, our model accelerates computations by approximately a million times, making it a substantial advancement for real-time predictive modeling in engineering applications. While it necessitates further testing on more intricate models, this framework shows substantial promise in elevating both efficiency and accuracy in engineering applications, particularly for real-time scenarios.
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