用元学习加速神经系统建模，融合物理约束提升精度。
Meta-Learning for Physically-Constrained Neural System Identification
- 基于梯度的元学习框架，快速适配黑箱系统模型。
- 仅需少量目标数据和少量迭代，即实现高精度状态估计。
- 适合物理约束强的现实场景，如定位与能源系统。
我们提出一种基于梯度的元学习框架，用于快速适应神经状态空间模型（NSSM）以进行黑箱系统识别。当适用时，我们还引入领域特定的物理约束，以提高NSSM的准确性。该方法的主要优势在于，不依赖单一目标系统的数据，而是利用来自多种源系统的数据，从而在目标数据有限且在线训练迭代次数少的情况下实现有效学习。通过基准案例，我们展示了该方法的潜力，研究了微调子网络而非全模型微调的效果，并报告了真实世界案例，以说明该方法在具有物理约束的实际问题中的应用价值与泛化能力。具体而言，我们证明了元学习模型在室内定位和能源系统中的基于模型状态估计任务中取得了更好的下游性能。
原文摘要 · Abstract (English)
We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the accuracy of the NSSM. The major benefit of our approach is that instead of relying solely on data from a single target system, our framework utilizes data from a diverse set of source systems, enabling learning from limited target data, as well as with few online training iterations. Through benchmark examples, we demonstrate the potential of our approach, study the effect of fine-tuning subnetworks rather than full fine-tuning, and report real-world case studies to illustrate the practical application and generalizability of the approach to practical problems with physical-constraints. Specifically, we show that the meta-learned models result in improved downstream performance in model-based state estimation in indoor localization and energy systems.
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