3 papers
math.OC2024
Koopman-based Control for Stochastic Systems: Application to Enhanced Sampling
Lei Guo, Jan Heiland, Feliks Nüske
We present a data-driven approach to use the Koopman generator for prediction and optimal control of control-affine stochastic systems. We provide a novel conceptual approach and a…
math.OC2024
Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design
Jan Heiland, Yongho Kim, Steffen W. R. Werner
Polytopic autoencoders provide low-di\-men\-sion\-al parametrizations of states in a polytope. For nonlinear PDEs, this is readily applied to low-dimensional linear parameter-varyi…
cs.LG2024
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows
Jan Heiland, Yongho Kim
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 autoencode…