Data Driven Reduced Order Modeling

Machine Learning for Reduced-Order Modeling (Chapter 14)

Data-Driven Fluid Mechanics - February 2023.

Applied Math Seminar - NYU Courant Mathematics

Data driven reduced order modeling for first order hyperbolic systems with application to waveform inversion. Speaker: Liliana Borcea, Columbia University.

PART 4: Reduced Order Models - data driven science & engineering

Reduced order models (ROMs) leverage POD modes for projecting PDE dynamics to low-rank subspaces where simulations of the governing PDE model can be more ...

Data-Driven Reduced-Order Modeling and Response Reconstruction

Data-Driven Reduced-Order Modeling and Response Reconstruction ; 2022 · Engineering Mechanics Institutes Conference (EMI 2022), Baltimore, Maryland, USA, 31 May - ...

Novel Data-driven Techniques in Reduced-Order Modeling of Fluid ...

Abstract. In this talk, we present a data-driven filtered reduced order model (DDF-ROM) framework for the numerical simulation of fluid flows.

[PDF] Data-driven reduced-order models via regularised Operator ...

P predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach that blends ...

A data-driven reduced-order modeling approach for parameterized ...

This paper proposed a data-driven non-intrusive model order reduction (NIMOR) approach for parameterized time-domain Maxwell's equations.

Data-driven reduced order modeling for time-dependent problems

Based on the full-order data, a reduced basis is constructed by the proper orthogonal decomposition (POD), and the maps between the time/parameter values and ...

Non-intrusive data-driven reduced-order modeling for time ...

This paper proposes and studies a non-intrusive reduced-order modeling approach for time-dependent parametrized problems. It is purely data- ...

Physics-informed data-driven reduced-order models for Dynamic ...

We use a physics-informed dynamic mode decomposition algorithm to reduce the model complexity in a way such that the physics of the wake mixing can be ...

Advancing the Field of Reduced-order Modeling – News

researchers have pursued projection-based reduced-order modeling—a technique that integrates ideas from data science, modeling, and simulation ...

Mengwu Guo on data-driven reduced order modeling - LinkedIn

It was an honor to host Mengwu Guo from University of Twente at our data-driven physical simulation (#DDPS) seminar, Lawrence Livermore ...

Data-driven reduced order modeling based on tensor ... - OUCI

Data-driven reduced order modeling based on tensor decompositions and its application to air-wall heat transfer in buildings · M. Azaïez · T. Chacón Rebollo · M.

Data-driven Reduced Order Model for prediction of wind turbine wakes

The ROM enables to capture the main physical processes underpinning the downstream evolution and dynamics of wind turbine wakes. The ROM is then embedded within ...

Data-driven reduced-order modeling through rational approximation ...

Data-driven reduced-order modeling through rational approximation and balancing: Loewner matrix approaches ... Data-driven reduced-order modeling ...

Data-driven reduced order modeling for time-dependent problems

Based on the full-order data, a reduced basis is constructed by the proper orthogonal decomposition (POD), and the maps between the time/parameter values and ...

"State Consistence of Data-Driven Reduced Order Models for ...

This paper investigates the state consistence of parametric data-driven reduced order models (ROMs) in a state-space form obtained by various system ...

Preserving Lagrangian Structure in Data-driven Reduced-order ...

Data-driven Reduced-order Modeling of. Large-scale Dynamical Systems. Harsh Sharma. Boris Kramer. Workshop and Conference on Nonlinear Model Reduction for ...

17w5140: Data-Driven Methods for Reduced-Order Modeling and ...

Workshop at the Banff International Research Station in Banff, Alberta between Jan 29 and Feb 3, 2017: Data-Driven Methods for Reduced-Order Modeling and ...

Reduced-Order Modeling of Reacting Flows Using Data-Driven ...

In this chapter, we review recent advances in ROM of turbulent reacting flows. We demonstrate the entire ROM workflow with a particular focus on obtaining the ...