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arXiv:2609.36712v1 Announce Type: cross Abstract: Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this chall...
arXiv:2502.00550v2 Announce Type: replace Abstract: Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design...
arXiv:2609.24117v1 Announce Type: new Abstract: Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system id...
arXiv:2609.26021v1 Announce Type: new Abstract: Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing opt...
arXiv:2607. 22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control.
arXiv:2608. 13510v1 Announce Type: cross Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency.