- What is data|driven model reduction🔍
- Dynamic data|driven reduced|order models🔍
- Data|driven model reduction for weakly nonlinear systems🔍
- Data|Driven Model Reduction🔍
- Dynamic data|driven model reduction🔍
- 7 Data|driven methods for reduced|order modeling🔍
- Data|Driven Model Reduction Strategies for Dynamical Systems🔍
- Amidst Data|Driven Model Reduction and Control🔍
What is data|driven model reduction
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 methods ...
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 ...
Data-driven model reduction for weakly nonlinear systems: A summary
One approach is data-driven reduction based on system data which are either measured or computed. In this regard the Loewner framework is a powerful tool for ...
Data-Driven Model Reduction, Scientific Frontiers, and Applications
This workshop brings together experts working on mathematical, statistical, computational, and engineering aspects of model reduction to share their research ...
Dynamic data-driven model reduction - Karen E. Willcox
Keywords: model reduction; online adaptivity; dynamic data-driven reduced models; incomplete sensor data; gappy proper orthogonal decomposition; dynamic ...
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 ...
Dynamic data-driven model reduction: adapting reduced models ...
We introduce a dynamic data-driven adaptation approach that adapts the reduced model from incomplete sensor data obtained from the system during the online ...
Data-Driven Model Reduction, Scientific Frontiers, and Applications ...
Data-driven modeling is a cornerstone for many applications. Finding appropriate scale/level models conditioned to the data requires some ...
Data-Driven Model Reduction Strategies for Dynamical Systems
In this study, we focus on data driven model reduction strategies for various biological systems where only observable data is available and illustrate their ...
Dynamic data-driven model reduction: adapting reduced models ...
This work presents a data-driven online adaptive model reduction approach for systems that undergo dynamic changes.
Amidst Data-Driven Model Reduction and Control - IEEE Xplore
In this note, we explore a middle ground between data-driven model reduction and data-driven control.
Fast data-driven model reduction for nonlinear dynamical systems
Due to its explicit coefficient fitting, fastSSM achieves a major speedup, and enables analysis of significantly higher-dimensional data than ...
Data-driven model reduction of agent-based systems using ... - arXiv
Title:Data-driven model reduction of agent-based systems using the Koopman generator ... Abstract:The dynamical behavior of social systems can be ...
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 ...
Data-Driven Model Reduction for Optimal Control of Large-scale ...
In the thesis, we investigate data-driven model reduction for optimal control of large-scale dynamical systems. Optimal control problems play an important ...
Rapid data-driven model reduction of nonlinear dynamical systems ...
Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ1-regularization ... Large-scale ...
Data-Driven Model Reduction by Moment Matching for Linear ...
We propose an algorithm, based on the so-called swapped interconnection, that (asymptotically) approximates an arbitrary number of moments of the system from a ...
Data-driven model order reduction for structures with piecewise ...
Prediction of the dynamic behavior of such systems is of great importance from practical and theoretical viewpoints. In this paper, a data- ...
Data-driven model reduction via non-intrusive optimization of ... - arXiv
In this paper, we address this issue by introducing a non-intrusive framework designed to simultaneously identify oblique projection operators and reduced- ...
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 ...