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

Learning Nonlinear Reduced Models from Data with Operator ...

Reduced modeling—also referred to as model reduction—learns patterns from training data of high-fidelity numerical simulations in order to.

Rapid data-driven model reduction of nonlinear dynamical systems ...

Large-scale nonlinear dynamical systems, such as models of atmospheric hydrodynamics, chemical reaction networks, and electronic circuits, ...

Fast data-driven model reduction for nonlinear dynamical systems

We present a fast method for nonlinear data-driven model reduction of dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs).

Equation-and Data-Driven Nonlinear Model Reduction to Spectral ...

Equation-and Data-Driven Nonlinear Model Reduction to Spectral Submanifolds by Prof. George Haller. 1.6K views · 2 years ago #SSM ...

PhD Position in Data-Driven Nonlinear Model Reduction

SSM-based modeling provides explicit and predictive polynomial ODE models for the observed dominant dynamics of nonlinear systems. A nontechnical ...

Fast data-driven model reduction for nonlinear dynamical systems

... Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds. Nat. Commun. 13(1), 1–13 (2022). https://doi.org/10.1038/s41467 ...

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

A data-driven nonlinear model reduction methodology based on spectral submanifolds (SSMs) takes observations of unforced nonlinear oscillations to construct ...

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. Schulze, Jan Christoph; Doncevic, ...

Data Driven Reduced Order Nonlinear Multiparametric MPC for ...

Multiparametric model predictive control (mp-MPC) obtains an off-line feedback control law using parametric programming. Control of distributed parameter ...

Papers with Code - Jan C. Schulze

We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. Model Predictive Control · Paper · Add Code · Data-Driven ...

7 Data-driven methods for reduced-order modeling - De Gruyter

The selection of observables (features) for the DMD/Koopman architecture can yield accurate low-dimensional embeddings for nonlinear partial differential ...

Data-Driven Model Reduction of Monotone Systems by Nonlinear ...

Abstract—In this paper, we develop data-driven model re- duction methods for monotone nonlinear control systems based on a nonlinear version of the dc gain.

Nonlinear Model Reduction for Control Workshop and Conference ...

Data-driven non-intrusive reduced-order modeling for plasma ... reduced-order model obtained by normal form reduction to generate the prediction.

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

... nonlinear model predictive control (NMPC). Data-driven model reduction offers a way to obtain low-order control models from complex digital ...

Nonlinear Model Predictive Control with Evolutionary Data-Driven ...

This linear MPC scheme is based on a straightforward approach that assumes a parametric model of crane dynamics, thereby reducing the identification task to ...

Data-driven modeling and complexity reduction for nonlinear ...

Data-driven modeling and complexity reduction for nonlinear systems with stability guarantees · Autonomous and Complex Systems · Dynamics and ...

Data-driven Model Reduction-based Nonlinear MPC for Large ...

Keywords: Proper Orthogonal Decomposition, Nonlinear Model Predictive Control, sequence of Artificial Neural Networks, Distributed Parameter Systems, control of.

Dynamic data-driven model reduction: Adapting reduced ... - MURI

Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting. Computer Methods in Applied Mechanics and Engineering, 289 ...

Data-Driven Nonlinear Reduced-Order Modeling for Solid and Fluid ...

Haller illustrates this on problems that include accelerated finite-element simulations of large structures, prediction of transitions in plane ...

Nonlinear model predictive control of a conductance-based neuron ...

We used recent advances in data-driven forecasting to construct a nonlinear machine-learning model of a Hodgkin–Huxley type neuron when only the membrane ...