Parameter estimation for dynamical systems remains challenging due to non-convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate and fast parameter estimation but do not exploit transferable knowledge across systems. To address this, we introduce a transfer-learning-based neural parameter estimation framework based on a pretraining-fine-tuning paradigm. This approach improves accuracy and eliminates the need for an initial parameter guess. We apply this framework to building RC thermal models, evaluating it against a Genetic Algorithm and a from-scratch neural baseline across eight simulated buildings, one real-world building, two RC model configurations, and four training data lengths. Results demonstrate an 18.6-24.0% performance improvement with only 12 days of training data and up to 49.4% with 72 days. Beyond buildings, the proposed method represents a new paradigm for parameter estimation in dynamical systems.

翻译：动力系统的参数估计因非凸性及对初始参数猜测的敏感性而具有挑战性。近期深度学习方法实现了准确快速的参数估计，但未能利用跨系统的可迁移知识。针对此问题，我们提出了一种基于预训练-微调范式的迁移学习神经参数估计框架。该方法提升了估计精度，并消除了对初始参数猜测的需求。我们将该框架应用于建筑RC热模型，在八组模拟建筑、一组真实建筑、两种RC模型配置及四种训练数据时长条件下，与遗传算法及从零训练的神经网络基线进行对比评估。结果表明，仅需12天训练数据即可实现18.6%-24.0%的性能提升，使用72天数据时提升幅度达49.4%。除建筑领域外，该方法为动力系统的参数估计提供了新范式。