English(EN) Orthogonal Discrepancy Kernels for Learning with Partial Physics

新框架将物理约束与差异学习解耦，用于系统辨识

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-03 04:00

研究人员推出了一种用于非线性系统辨识的新型半参数框架——正交差异核（ODKs）。该方法有效地将差异函数与基于物理的组件分离，即使在物理数据不完整的情况下也能实现更具可解释性的模型。该框架利用正交高斯过程回归来平衡稀疏参数选择与差异学习。 AI

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系统辨识的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Swapnil Manna, Timothy J. Rogers, Lawrence Bull · 2026-07-03 04:00

用于带部分物理约束的学习的正交差异核

arXiv:2606.21199v2 Announce Type: replace Abstract: We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Orthogonal Gaussian process regression balances sparse parameter selection (the w…