Our approach employs deep-learning methods and targets a minimal need of expert knowledge of model reduction. Koopman theory postulates that nonlinear dynamical ...

Jan 29, 2022 · We discuss that the Wiener structure is particularly suitable for model reduction, and can be naturally derived from Koopman theory. Moreover, ...

Our approach employs deep-learning methods and targets a minimal need of expert knowledge of model reduction. Koopman theory postulates that nonlinear dynamical ...

Our approach employs deep-learning methods and targets a minimal need of expert knowledge of model reduction. Koopman theory postulates that nonlinear dynamical ...

We use Koopman theory to develop a data-driven nonlinear model reduction and identification strategy for multiple-input multiple-output (MIMO) input-affine ...

Nov 10, 2022 · Identification of MIMO Wiener-type Koopman models for data-driven model reduction using deep learning. Schulze, J. C.RWTH* ; Doncevic, D.FZJ ...

Jan 9, 2024 · We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman ...

Jan 29, 2022 · We use Koopman theory to develop a data-driven nonlinear model reduction and identification strategy for multiple-input multiple-output ...

This respository contains the code for the paper Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep-Learning by J.

This respository contains the code for the paper Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep-Learning by J.