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English(EN) Generative Learning on Manifolds for Probabilistic Seismic Performance Assessment and Structural Identifiability

新的生成式学习框架增强了地震性能评估

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新的基于流形的生成式框架，该框架使用扩散图、基于分数的扩散模型和几何谐波来学习结构参数和地震响应轨迹的联合分布。该方法旨在降低地震性能评估的计算成本并改进不确定性量化。该框架应用于九层钢框架时，可以预测前向概率性结果并进行反向推理，以识别哪些结构特性对观测到的地震性能影响最大且最易辨识。 AI

影响 这项研究展示了生成式学习如何提高地震性能评估的效率和准确性，从而可能带来更稳健的结构设计。

排序理由 关于地震性能评估新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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新的生成式学习框架增强了地震性能评估

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关于地震性能评估新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dimitris G Giovanis, Ioannis G Kevrekidis, Roger G Ghanem ·

    流形上的生成式学习用于概率性地震性能评估和结构可辨识性

    arXiv:2611.12908v1 Announce Type: cross Abstract: Seismic performance assessment relies on computationally expensive nonlinear simulations, limiting uncertainty quantification and inference of structural properties from observed responses. We develop a manifold-based generative f…

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