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, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification -- one notable example is given by Gaussian process
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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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