Data|Driven Model Reduction and Nonlinear Model Predictive ...

[2309.05386] Data-Driven Model Reduction and Nonlinear ... - arXiv

Achieving real-time capability is an essential prerequisite for the industrial implementation of nonlinear model predictive control (NMPC). Data ...

Data-Driven Model Reduction and Nonlinear Model Predictive ...

Data-Driven Model Reduction and Nonlinear Model Predictive Control of an. Air Separation Unit by Applied Koopman Theory. Jan C. Schulze a, Danimir T. Doncevic ...

Data-driven Nonlinear Model Reduction using Koopman Theory

A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of ...

What is data-driven model reduction - Karen E. Willcox

Our data-driven model reduction techniques apply to general linear and nonlinear problems. In the linear setting, our (dynamic) data-driven reduced models ...

Data-driven modeling and prediction of non-linearizable dynamics ...

Low-dimensional reduced models of high-dimensional nonlinear dynamical systems are critically needed in various branches of applied science and ...

Nonlinear model reduction from equations and data - AIP Publishing

G. Haller. , “. Data-driven modeling and forecasting of chaotic dynamics on inertial manifolds constructed as spectral submanifolds. ,”. Chaos.

Fast data-driven model reduction for nonlinear dynamical systems

Model simplicity (or parsimony) is vital for interpretability, control, and response prediction for mechanical devices [31]. This has motivated ...

A data-driven approach to nonlinear model reduction - Boris Kramer

In these settings, a predictive simulation of the system must be evaluated many times, so traditional simulation methods based on high-dimensional spatial ...

Data-driven model reduction for weakly nonlinear systems: A summary

Model reduction seeks to replace complex dynamical systems with simpler ones, having similar characteristics. One approach is data-driven reduction based on ...

Data-driven Nonlinear Model Reduction using Koopman Theory

A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity ...

Data-Driven Nonlinear Model Reduction Using Koopman Theory

This work proposes generic model structures combining delay-coordinate encoding of measurements and full state decoding to integrate reduced Koopman modeling ...

Nonlinear Reduced-Order Modeling from Data by Prof. George Haller.

... data-driven reduced-order modeling of nonlinear phenomena. Specifically, spectral submanifolds (SSMs) represent very low-dimensional ...

Learning Nonlinear Reduced Models from Data with Operator ...

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by ...

Data-driven nonlinear model reduction to spectral submanifolds in ...

This article is part of the theme issue 'Data-driven prediction in dynamical systems'. 1. Introduction. Dimensionality reduction for datasets ...

Data-driven model reduction-based nonlinear MPC for large-scale ...

We present a new reduced nonlinear MPC method for large-scale input/output systems. The method works with black-box solvers and/or with experimental ...

What is nonlinear model reduction - Karen E. Willcox

Nonlinear model reduction uses projection to derive low-cost approximate models of nonlinear systems.

Data-driven model reduction-based nonlinear MPC for large-scale ...

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally ...

Data-Driven Nonlinear Model Reduction Using Koopman Theory

... models. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive ...

Data-Driven Nonlinear Model Reduction Using Koopman Theory

MODEL order reduction is a powerful technique to achieve real-time nonlinear model predictive control. (NMPC) when using large-scale process models [1].

A data-driven approach to nonlinear model reduction - AIAA ARC

Transform & Learn: A data-driven approach to nonlinear model reduction. Elizabeth Qian,; Boris Kramer,; Alexandre N. Marques and ...