研究人员开发了一种自适应主干DeepONet模型，以改进长跨度公路桥梁局部结构响应的预测。该新框架使用k近邻（KNN）策略动态创建依赖于载荷的学习域，使网络能够专注于关键结构影响区域。该模型结合了距离感知特征和基于物理的重建方法，实现了与有限元分析（FEM）相当的精度，同时显著缩短了计算时间，总体速度提高了60倍，仅推理速度就提高了四个数量级。 AI
影响 该AI模型显著加速了桥梁的结构分析，为更快的数字孪生应用和基础设施评估提供了可能。
排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种用于结构分析的新AI模型。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.20015v1 Announce Type: new Abstract: Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twin…
Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twins. Existing SciML approaches struggle to accurat…