26 citations · 46 across the 10 of their papers we have counts for
11 papers
High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
Diana A. Bistrian
The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin proj…
Hankel-Koopman Finite-Horizon Energy Decomposition of Coupled Experimental Data: A Three-Phase Data-Driven Twin Forecasting Framework
Diana A. Bistrian, Marcel Topor
This paper introduces a unified three-phase data-driven twin framework for finite-horizon forecasting of physical quantities from coupled experimental measurements. The framework c…
Reduced Order Data-driven Twin Models for Nonlinear PDEs by Randomized Koopman Orthogonal Decomposition and Explainable Deep Learning
D. A. Bistrian
This study introduces a data-driven twin modeling framework based on modern Koopman operator theory, offering a significant advancement over classical modal decomposition by accura…
Mathematical Considerations on Randomized Orthgonal Decomposition Method for Developing Twin Data Models
Diana A. Bistrian
This paper introduces the approach of Randomized Orthogonal Decomposition (ROD) for producing twin data models in order to overcome the drawbacks of existing reduced order modellin…
Reduced-order modelling based on Koopman operator theory
Diana A. Bistrian, Gabriel Dimitriu, Ionel M. Navon
The present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output u…
Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning
Diana Alina Bistrian, Omer San, Ionel Michael Navon
A digital twin is a surrogate model that has the main feature to mirror the original process behavior. Associating the dynamical process with a digital twin model of reduced comple…
Sketching Methods for Dynamic Mode Decomposition in Spherical Shallow Water Equations
Shady E. Ahmed, Omer San, Diana A. Bistrian +1
Dynamic mode decomposition (DMD) is an emerging methodology that has recently attracted computational scientists working on nonintrusive reduced order modeling. One of the major st…
Sampling and resolution characteristics in reduced order models of shallow water equations: intrusive vs non-intrusive
Shady E. Ahmed, Omer San, Diana A. Bistrian +1
We investigate the sensitivity of reduced order models (ROMs) to training data resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD)…
Randomized Dynamic Mode Decomposition for Non-Intrusive Reduced Order Modelling
D. A. Bistrian, I. M. Navon
This paper focuses on a new framework for reduced order modelling of non-intrusive data with application to 2D flows. To overcome the shortcomings of intrusive model order reductio…
Spectral Differentiation Operators and Hydrodynamic Models for Stability of Swirling Fluid Systems
Diana Alina Bistrian, Florica Ioana Dragomirescu, George Savii
In this paper we develop hydrodynamic models using spectral differential operators to investigate the spatial stability of swirling fluid systems. Including viscosity as a valid pa…
Spectral Techniques for Solving PDE Stability Model of Vortex Rope
Diana Alina Bistrian
In this paper spectral methods are applied to investigate the hydrodynamic instability of swirling flow with application to Francis hydraulic turbine. Spectral methods imply repres…