arXiv:2606. 11629v1 Announce Type: cross Abstract: This manuscript proposes an integral formulation of the newly defined quadratic embedding method for identifying nonlinear systems (QENDy).
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
This manuscript proposes an integral formulation of the newly defined quadratic embedding method for identifying nonlinear systems (QENDy). In the original algorithm, trajectory data points along with their time derivatives are used.
arXiv:2607. 15077v1 Announce Type: new Abstract: Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known.
arXiv:2606. 15690v1 Announce Type: new Abstract: Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity.
arXiv:2606. 23827v1 Announce Type: cross Abstract: A data-driven method is developed for approximating value functions in deterministic optimal control problems with nonlinear control-affine dynamics.
arXiv:2606. 24769v1 Announce Type: cross Abstract: Even when Dirac-Frenkel dynamics determine a well-defined evolution in function space, the corresponding parameter dynamics can be non-unique or ill-conditioned for redundant nonlinear parametrizations such as neural networks or mixture models.
arXiv:2606. 00298v1 Announce Type: cross Abstract: Data-driven reduced-order modeling is an essential component in the computer-aided design of control systems.