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Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
Previous work has shown that using such informative priors to regularize probabilistic deep learning (DL) models increases their performance and data-efficiency.
Informed Priors for Knowledge Integration in Trajectory Prediction
We show that our method outperforms both non-informed and informed learning methods, that are often used in the literature.
Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC
Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used for decades in many engineering applications.
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