• Nonlinear model reduction from equations and data🔍
• Information theory for data|driven model reduction in physics and ...🔍
• Data|driven model reduction of agent|based systems using ...🔍
• Data Driven Reduced Order Modeling🔍
• Adaptive Data|Driven Model Order Reduction for Unsteady ...🔍
• Data|Driven Parametrized Model Reduction in the Loewner ...🔍
• Data|driven discovery and model reduction of complex systems🔍
• adapting reduced models from incomplete data🔍

Nonlinear model reduction from equations and data - AIP Publishing

In this context, data-driven model reduction has the potential to address these challenges by providing low-dimensional approximations of ...

Information theory for data-driven model reduction in physics and ...

Model reduction is the construction of simple yet predictive descriptions of the dynamics of many-body systems in terms of a few relevant ...

Data-driven model reduction of agent-based systems using ... - PLOS

The name was coined by Holley and Liggett [4] a few years later. In this model, an agent imitates the opinion of its neighbors. This means that whenever two ...

Data Driven Reduced Order Modeling - Sites at USC

Data Driven Reduced Order Modeling ... A multitude of dynamical systems are described by a set of a large number of nonlinear differential equations which poses ...

Adaptive Data-Driven Model Order Reduction for Unsteady ... - MDPI

A data-driven adaptive reduced order modelling approach is presented for the reconstruction of impulsively started and vortex-dominated flows.

Data-Driven Parametrized Model Reduction in the Loewner ...

The main tools are a new barycentric formula and Loewner matrices formed directly from measurements. This data-driven approach introduces a new degree of ...

Data-driven discovery and model reduction of complex systems

We propose a simple physics-based approach which learns a model for the underlying relationships between sensors. Online, the method predicts future sensor ...

adapting reduced models from incomplete data - De Anza College

This work presents a data-driven online adaptive model reduction approach for systems that undergo dynamic changes. Classical model reduction constructs a ...

(PDF) Dynamic data-driven model reduction: adapting reduced ...

We introduce a dynamic data-driven adaptation approach that adapts the reduced model from incomplete sensor data obtained from the system during ...

Data-Driven Control: The Goal of Balanced Model Reduction

In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly controllable and observable, to ...

Fast data-driven model reduction for nonlinear dynamical systems

Research Collection · Fast data-driven model reduction for nonlinear dynamical systems. Mendeley · CSV · RIS · BibTeX · Browse · Publish · Statistics. Downloads ...

DATA-DRIVEN REDUCED-ORDER MODELING FOR ... - OpenMETU

DATA-DRIVEN REDUCED-ORDER MODELING FOR COMPUTATIONAL FLUID DYNAMICS ... This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 ...

Data-Driven Model Reduction for Stochastic Burgers Equations - MDPI

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, ...

Data‐driven model reduction for the Bayesian solution of inverse ...

This paper proposes a data-driven projection-based model reduction technique to reduce this computational cost.

Amidst Data-Driven Model Reduction and Control

We illustrate how the derived family of reduced models can be used for data-driven control of the original system under suitable conditions. Finding a control.

Extending the Capabilities of Data-Driven Reduced-Order Models to ...

The method extends current capabilities of reduced-order models to generalise, i.e., to make predictions for unseen scenarios. The method is applied to a 2D ...

Model reduction · JuliaSimControl - JuliaHub

Reduced-order models require less effort to simulate and may improve the numerical performance of some algorithms. Model reduction of linear time-invariant ...

Dynamic data-driven model reduction: adapting reduced models ...

Keywords: Model reduction, Online adaptivity, Dynamic data-driven reduced models,. Incomplete sensor data, Gappy proper orthogonal decomposition, Dynamic.

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 ...

Data-driven structure-preserving model reduction for stochastic ...

The purpose of this work is twofold: to demonstrate that the conventional SVD-based model reduction methods for ordinary differential equations (ODEs) can be ...