Scholarlink

<p>Traditional Physics-Informed Neural Networks (PINNs) utilize a fully-connected architecture, causing shared parameters among outputs and subsequent accuracy degradation. We proposed a MultiSubPINN model to address this by deploying independent networks for distinct predictions, minimizing output interference and preserving individual output optimizations. Our results demonstrate that MultiSubPINN significantly outperforms conventional PINN models in solving complex partial differential equations (PDEs). Notably, MultiSubPINN achieved a reduction in Mean Square Error (MSE) by 32.7%, 9.9%, and 52.8% for shear stress, viscosity, and structure parameter predictions, respectively. Moreover, we introduce a novel physics-based sampling strategy that leverages residual distribution and physics principles to further refine MultiSubPINN’s performance. This approach markedly surpasses random sampling strategies, reducing MSE by approximately 97.7%, 90.2%, and 38.3% for the aforementioned parameters. Importantly, the enhancements in prediction accuracy are achieved with a marginal increase (0.1%) in training time, underscoring MultiSubPINN’s efficacy and potential for broader application in material simulation domains.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Physics informed neural network with multiple subnetworks for predicting rheological behavior of cementitious materials

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

查看全文
  • Tianjie Zhang,
  • Donglei Wang,
  • Yang Lu

摘要

Traditional Physics-Informed Neural Networks (PINNs) utilize a fully-connected architecture, causing shared parameters among outputs and subsequent accuracy degradation. We proposed a MultiSubPINN model to address this by deploying independent networks for distinct predictions, minimizing output interference and preserving individual output optimizations. Our results demonstrate that MultiSubPINN significantly outperforms conventional PINN models in solving complex partial differential equations (PDEs). Notably, MultiSubPINN achieved a reduction in Mean Square Error (MSE) by 32.7%, 9.9%, and 52.8% for shear stress, viscosity, and structure parameter predictions, respectively. Moreover, we introduce a novel physics-based sampling strategy that leverages residual distribution and physics principles to further refine MultiSubPINN’s performance. This approach markedly surpasses random sampling strategies, reducing MSE by approximately 97.7%, 90.2%, and 38.3% for the aforementioned parameters. Importantly, the enhancements in prediction accuracy are achieved with a marginal increase (0.1%) in training time, underscoring MultiSubPINN’s efficacy and potential for broader application in material simulation domains.