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Found 6 papers, 1 papers with code
Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design
Polytopic autoencoders provide low-dimensional parametrizations of states in a polytope.
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows
With the advancement of neural networks, there has been a notable increase, both in terms of quantity and variety, in research publications concerning the application of autoencoders to reduced-order models.
Frequency-dependent Switching Control for Disturbance Attenuation of Linear Systems
We show that our FDSC scheme is capable to approximate the solid in-band performance while maintaining acceptable out-of-band performance with regard to global time horizons as well as localized time horizons.
Convolutional Autoencoders, Clustering and POD for Low-dimensional Parametrization of Navier-Stokes Equations
Simulations of large-scale dynamical systems require expensive computations.
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.
Solution Formulas for Differential Sylvester and Lyapunov Equations
The differential Sylvester equation and its symmetric version, the differential Lyapunov equation, appear in different fields of applied mathematics like control theory, system theory, and model order reduction.
Numerical Analysis 15A24, 65F60, 65L05
