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English(EN) Trajectory Design and Budgeted Querying for Digital Twin Calibration

新框架通过预算数据采集优化数字孪生校准

研究人员开发了一个新的数字孪生(物理系统的虚拟副本)校准框架。该框架通过优化交互数据的生成并战略性地使用有限的预算进行特权参数测量,解决了数据收集成本高昂的挑战。该方法结合了强化学习控制器、循环参数估计器和预算查询策略。在 Pendulum 和 Waterworld 模拟上的案例研究表明,与未校准的孪生相比,该方法在数据稀疏场景下能有效降低误差。 AI

影响 这项研究可能导致更高效、更准确的数字孪生校准,从而降低复杂系统中数据收集的成本和时间。

排序理由 该集群包含一篇详细介绍数字孪生校准新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架通过预算数据采集优化数字孪生校准

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该集群包含一篇详细介绍数字孪生校准新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vladyslava Spitkovska, Dmytro Kuzmenko ·

    数字孪生校准的轨迹设计与预算查询

    arXiv:2608.08631v1 Announce Type: new Abstract: Digital-twin calibration requires interaction data that is expensive to collect. We study two acquisition decisions: which trajectories to generate, and when to spend a limited budget on privileged parameter measurements. Our framew…