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Found 4 papers, 1 papers with code
Stability-Certified Learning of Control Systems with Quadratic Nonlinearities
To this aim, we investigate the stability characteristics of control systems with energy-preserving nonlinearities, thereby identifying conditions under which such systems are bounded-input bounded-state stable.
Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference
In this work, we propose inference formulations to learn quadratic models, which are stable by design.
Inference of Continuous Linear Systems from Data with Guaranteed Stability
Machine-learning technologies for learning dynamical systems from data play an important role in engineering design.
Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flows
To that end, we utilize the intrinsic structure of the Navier-Stokes equations for incompressible flows and show that learning dynamics of the velocity and pressure can be decoupled, thus leading to an efficient operator inference approach for learning the underlying dynamics of incompressible flows.
