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English(EN) Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements

新的SINDy方法从嘈杂、多保真度数据中发现动力学系统

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Multi-Fidelity SINDy的新方法，用于从具有不同噪声和保真度级别的数据中发现非线性动力学系统。该方法通过结合Ensemble SINDy和Weak SINDy以及源自广义最小二乘法的加权回归，扩展了现有的非线性动力学系统稀疏识别（SINDy）框架。该方法已在包括常微分方程和偏微分方程在内的基准系统上得到验证，并证明了其在预测双摆系统动力学方面的有效性。研究结果表明，这种多保真度集成可以通过减轻异方差噪声的影响来改善模型恢复，即使利用了较低质量的测量。 AI

排序理由 该集群包含一篇详细介绍新科学方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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新的SINDy方法从嘈杂、多保真度数据中发现动力学系统

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Filippo Zacchei, Ana Larra\~naga, Attilio Frangi, Andrea Manzoni, Steven L. Brunton ·

    多保真度SINDy：具有保真度加权测量的非线性动力学系统的稀疏发现

    arXiv:2606.15690v1 Announce Type: new Abstract: Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity. Measurement uncertainty may vary across repeated observations, sensing devices, or even within a single experiment. This…

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