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

Data Driven Reduced Order Modeling - Sites at USC

A multitude of dynamical systems are described by a set of a large number of nonlinear differential equations which poses significant challenges in model ...

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

of data-driven nonlinear modeling and nonlinear complexity reduction tools with stability guarantees forms the scope of this thesis. 1.2 ...

Data-driven nonlinear model reduction by moment-matching for the ...

Abstract—Given the relevance of control-oriented models in optimal control design for wave energy converters (WECs),.

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

Data-Driven Nonlinear Reduced-Order Modeling for Solid and Fluid Mechanics ... Machine learning has been a major development in applied science and engineering, ...

Model Order Reduction and Data-Driven Computational Modeling ...

Nevertheless, MOR has proven to be significantly more difficult for parameterized mechanics systems that exhibit a wide variety of parameter-dependent nonlinear ...

‎Data-driven Reduced Order Modeling and Model Updating of ...

Reduced order models (ROMs) are a highly efficient alternative to full-order finite element models (FEM) of geometrically nonlinear structures. Many non- ...

haller-group/SSMLearn: Data-driven reduced order modeling for ...

Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds. ... Data-driven nonlinear model reduction to spectral submanifolds in ...

Data-driven nonlinear model reduction to spectral - George Haller

One contribution of 16 to a theme issue. 'Data-driven prediction in dynamical systems'. Subject Areas: mechanical engineering, computational mathematics, ...

Nonlinear model predictive control for distributed parameter systems ...

2 TSCRNN FOR MODEL REDUCTION OF DISTRIBUTED PARAMETER SYSTEMS. For the data-driven model reduction of DPSs, knowledge of the governing PDEs ...

Data-Driven Nonlinear Model Predictive Control of an Autonomous ...

... reducing travel time, and increasing fuel efficiency. With traditional control techniques, models for Uncrewed Surface Vessels (USVs) do not ...

Non-intrusive nonlinear model reduction via machine learning ...

The leading alternative involves black-box system identification, which is purely data-driven, but generally yields uninterpretable models. In ...

Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced ...

Arbabi, H., Korda, M., Mezic, I.: A data-driven Koopman model predictive control framework for nonlinear flows (2018). arXiv:1804.05291; Bellmann, R.E. ...

(744c) Nonlinear Model Predictive Control of Air Separation ... - AIChE

To this end, we perform model reduction to derive a controller model ... Economic nonlinear model predictive control using hybrid mechanistic data-driven models ...

Amidst Data-Driven Model Reduction and Control

While nonlinear systems are arguably the most rele- vant dynamics for data-driven applications, they also pose extremely challenging problems. A fundamental ...

Sparse Identification of Nonlinear Dynamics-Based Feature ...

These models are then used for data-driven model predictive control of a buck switch mode power supply to find the optimal duty cycle that ...

Online Adaptive Model Reduction for Nonlinear Systems via Low ...

The online adaptation uses new data to produce a reduced system that accurately approximates behavior not anticipated in the offline phase. These online data ...

Model Reduction and Nonlinear Model Predictive Control of Large ...

Model Reduction and Nonlinear Model Predictive Control of Large-Scale Distributed Parameter Systems with Applications in Solid Sorbent-Based ...

Learning Lyapunov terminal costs from data for complexity reduction ...

constrained systems, data-driven control, neural networks, nonlinear model predictive control. 1. INTRODUCTION. Nonlinear model predictive ...

SINDy with Control: A Tutorial - Urban Fasel

this data-driven control framework to arbitrary nonlinear systems. Keywords: Model predictive control, data-driven models, ma- chine learning, system ...

Data-driven reduced-order models via regularised Operator ...

This paper derives predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach.