论文标题
学习限制的动态，高斯原理遵守高斯流程
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
论文作者
论文摘要
机械系统约束动力学的识别通常具有挑战性。学习方法有望减轻分析分析，但需要大量的培训数据。我们建议将分析力学的见解与高斯过程回归相结合，以提高模型的数据效率和约束完整性。结果是一个高斯过程模型，该模型结合了先验约束知识，以使其预测遵循高斯最少约束的原理。作为回报，对系统加速度的预测自然尊重潜在的非理想（非）载体平等约束。作为推论的结果，我们的模型可以从受约束系统的数据中推断出不受约束的系统的加速度，并在不同的约束配置之间实现知识传输。
The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.
