ガウスの最小拘束原理に従うガウス過程による拘束力学の学習
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
解析力学の知見とガウス過程回帰を組み合わせ、拘束条件を満たす加速度予測を少ないデータで学習するモデルを提案した。
著者: A. Rene Geist, Sebastian Trimpe
分類: cs.LG, cs.RO, cs.SY, eess.SY, stat.ML
原文アブストラクト
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.
