Sridhar Chellappa
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View article: Discrete empirical interpolation in the tensor t-product framework
Discrete empirical interpolation in the tensor t-product framework Open
The discrete empirical interpolation method (DEIM) is a well-established approach, widely used for state reconstruction using sparse sensor/measurement data, nonlinear model reduction, and interpretable feature selection. We introduce the …
GS-PINN: Greedy Sampling for Parameter Estimation in Partial Differential Equations Open
Partial differential equation parameter estimation is a mathematical and computational process used to estimate the unknown parameters in a partial differential equation model from observational data. This paper employs a greedy sampling a…
A posteriori error estimation for model order reduction of parametric systems Open
This survey discusses a posteriori error estimation for model order reduction of parametric systems, including linear and nonlinear, time-dependent and steady systems. We focus on introducing the error estimators we have proposed in the pa…
Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure Open
Accurate error estimation is crucial in model order reduction, to obtain small reduced-order models as well as to certify their accuracy when deployed in downstream applications such as digital twins. In existing a posteriori error estimat…
Fast and Reliable Reduced-Order Models for Cardiac Electrophysiology Open
Mathematical models of the human heart are increasingly playing a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease. The aim is to aid medical practitioners diagnose and trea…
A Posteriori Error Estimation for Model Order Reduction of Parametric Systems Open
This survey discusses a posteriori error estimation for model order reduction of parametric systems, including linear and nonlinear, time-dependent and steady systems. We focus on introducing the error estimators we have proposed in the pa…
Fast and reliable reduced‐order models for cardiac electrophysiology Open
Mathematical models of the human heart increasingly play a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease. The ultimate aim is to aid medical practitioners diagnose and tr…
Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure Open
Accurate error estimation is crucial in model order reduction, both to obtain small reduced-order models and to certify their accuracy when deployed in downstream applications such as digital twins. In existing a posteriori error estimatio…
Fast A <i>Posteriori</i> State Error Estimation for Reliable Frequency Sweeping in Microwave Circuits via the Reduced-Basis Method Open
We develop a compact, reliable model order reduction approach for fast\nfrequency sweeps in microwave circuits by means of the reduced-basis method.\nContrary to what has been previously done, special emphasis is placed on\ncertifying the …
Fast A Posteriori State Error Estimation for Reliable Frequency Sweeping in Microwave Circuits via the Reduced-Basis Method Open
We develop a compact, reliable model order reduction approach for fast frequency sweeps in microwave circuits by means of the reduced-basis method. Contrary to what has been previously done, special emphasis is placed on certifying the acc…
Inf-Sup-Constant-Free State Error Estimator for Model Order Reduction of Parametric Systems in Electromagnetics Open
A reliable model order reduction process for parametric analysis in electromagnetics is detailed. Special emphasis is placed on certifying the accuracy of the reduced-order model. For this purpose, a sharp state error estimator is proposed…
Adaptive basis construction and improved error estimation for parametric nonlinear dynamical systems Open
Summary An adaptive scheme to generate reduced‐order models for parametric nonlinear dynamical systems is proposed. It aims to automatize the proper orthogonal decomposition (POD)‐Greedy algorithm combined with empirical interpolation. At …
An Adaptive Sampling Approach for the Reduced Basis Method Open
The offline time of the reduced basis method can be very long given a large training set of parameter samples. This usually happens when the system has more than two independent parameters. On the other hand, if the training set includes f…
Adaptive POD-DEIM model reduction based on an improved error estimator Open
For reliable, efficient, rapid simulations of dynamical systems, a reduced order model (ROM) with certified accuracy is highly desirable. The ROM is derived from a full order model (FOM) through model reduction. In this work, we propose an…