145 citations · 718 across the 19 of their papers we have counts for
19 papers
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…
CAMERA: A Method for Cost-aware, Adaptive, Multifidelity, Efficient Reliability Analysis
S. Ashwin Renganathan, Vishwas Rao, Ionel M. Navon
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails…
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes
Claire E. Heaney, Zef Wolffs, Jón Atli Tómasson +7
The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over…
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…
A data driven reduced order model of fluid flow by Auto-Encoder and self-attention deep learning methods
R. Fu, D. Xiao, I. M. Navon +1
This paper presents a new data-driven non-intrusive reduced-order model(NIROM) that outperforms the traditional Proper orthogonal decomposition (POD) based reducedorder model. This…
A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations
Suraj Pawar, Omer San, Adil Rasheed +1
This work presents a hybrid modeling approach to data-driven learning and representation of unknown physical processes and closure parameterizations. These hybrid models are suitab…
Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
Suraj Pawar, Shady E. Ahmed, Omer San +2
Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast cov…
An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling
M. Cheng, F. Fang, C. C. Pain +1
Considering the high computation cost produced in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in r…
Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
M. Cheng, F. Fang, C. C. Pain +1
Deep learning techniques for improving fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly p…
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)…
Memory embedded non-intrusive reduced order modeling of non-ergodic flows
Shady E. Ahmed, Sk. Mashfiqur Rahman, Omer San +2
Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniq…
Goal-based sensitivity maps using time windows and ensemble perturbations
C. E. Heaney, P. Salinas, F. Fang +2
We present an approach for forming sensitivity maps (or sensitivites) using ensembles. The method is an alternative to using an adjoint, which can be very challenging to formulate…
On Triangle Inequality Based Approximation Error Estimation
A. K. Alekseev, A. E. Bondarev, I. M. Navon
The distance between the true and numerical solutions in some metric is considered as the discretization error magnitude. If error magnitude ranging is known, the triangle inequali…
On Estimation of Discretization Error Norm via Ensemble of Approximate Solutions
A. K. Alekseev, A. E. Bondarev, I. M. Navon
The issue of single-grid discretization error estimator, operating in the postprocessor mode, is addressed in the paper. An ensemble of numerical solutions, obtained using solvers…
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…
Comparison of POD reduced order strategies for the nonlinear 2D Shallow Water Equations
Răzvan Ştefănescu, Adrian Sandu, Ionel M. Navon
This paper introduces tensorial calculus techniques in the framework of Proper Orthogonal Decomposition (POD) to reduce the computational complexity of the reduced nonlinear terms.…
POD/DEIM Reduced-Order Strategies for Efficient Four Dimensional Variational Data Assimilation
Răzvan Ştefănescu, Adrian Sandu, Ionel Michael Navon
This work studies reduced order modeling (ROM) approaches to speed up the solution of variational data assimilation problems with large scale nonlinear dynamical models. It is show…
1D+4D-VAR data assimilation of lightning with WRFDA system using nonlinear observation operators
Razvan Stefanescu, I. Michael Navon, Henry Fuelberg +1
This paper addresses the impact of assimilating data from the Earth Networks Total Lightning Network (ENTLN) during two cases of severe weather. Data from the ENTLN serve as a subs…
POD/DEIM Nonlinear model order reduction of an ADI implicit shallow water equations model
Razvan Stefanescu, Ionel Michael Navon
In the present paper we consider a 2-D shallow-water equations (SWE) model on a -plane solved using an alternating direction fully implicit (ADI) finite-difference scheme on a r…