Data Driven Reduced Order Modeling

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

Data-driven model reduction constructs reduced-order models of large-scale systems by learning the system response characteristics from data. Existing ...

Dynamic data-driven reduced-order models - ScienceDirect.com

Dynamic reduced-order models exploit the opportunity presented by dynamic sensor data and adaptively incorporate sensor data during the online phase. This ...

Reduced Order Modeling - MATLAB & Simulink - MathWorks

Reduced order modeling (ROM) and model order reduction (MOR) are techniques for reducing the computational complexity of a full-order, high-fidelity model.

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

In addition to DMD/Koopman decompositions, coarse-grained models for spatio-temporal systems can also be discovered using the sparse identification of nonlinear ...

Data-Driven Reduced-Order Model for Bubbling Fluidized Beds

This work developed a pioneering data-driven reduced-order model (ROM) for efficient modeling of dense gas–solid flow in bubbling fluidized beds ...

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 ...

A data-driven reduced-order model for stiff chemical kinetics using ...

Abstract. A data-based reduced-order model (ROM) is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing ...

Data-driven reduced-order modeling for nonlinear aerodynamics ...

We propose a novel approach based on machine learning (ML) to construct reduced-order models (ROMs) using an autoencoder neural network coupled with a discrete ...

Physics-based machine learning and data-driven reduced-order ...

This thesis considers the task of learning efficient low-dimensional models for dynamical systems. To be effective in an engineering setting, these models ...

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 ...

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 ...

Data-Driven Filtered Reduced Order Modeling of Fluid Flows

Abstract. We propose a data-driven filtered reduced order model (DDF-ROM) framework for the numerical simulation of fluid flows. The novel DDF-ROM framework ...

Data-driven reduced order modeling for mechanical oscillators ...

This article studies the potential of HAVOK for application to mechanical oscillators by investigating which information of the underlying system can be ...

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 that ...

[2107.02784] Data-driven reduced order modeling of environmental ...

We investigate employing deep autoencoders for discovering the reduced basis representation, the dynamics of which are then approximated by NODE.

Reduced Order Models (ROMs) (Chapter 11) - Data-Driven Science ...

Related content · Reduced-order model for efficient generation of a subsonic missile's aerodynamic database · Adaptive Dimensionality-Reduction for Time- ...

A data-driven reduced-order model for rotor optimization - WES

In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD)-based reduced-order model (ROM) for predicting rotor ...

Data-driven reduced order modeling for parametrized time ...

The reduced-order model is commonly data-driven and features an offline–online framework.8 In particular, the empirical modes extracted based on ...

Dynamic data-driven model reduction - Karen E. Willcox

Dynamic data-driven reduced models adapt directly from sensor data to changes in the latent parameters (i.e., external influence), without recourse to the full.

When data driven reduced order modeling meets full waveform ...

Title:When data driven reduced order modeling meets full waveform inversion ... Abstract:Waveform inversion is concerned with estimating a ...