2 citations · 9 across the 15 of their papers we have counts for
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An Iterative Active Subspace Approach for Model Order Reduction of Parametric Systems with High-Dimensional Parameter Spaces
Chenzi Wang, Peizhi Yu, Lihong Feng +3
The increasing complexity in design and manufacturing has driven the need for advanced techniques for fast modeling problems with large-dimensional parameter spaces. Avoiding high-…
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems
Harshit Kapadia, Peter Benner, Lihong Feng
In situations where the solution of a high-fidelity dynamical system needs to be evaluated repeatedly, over a vast pool of parametric configurations and in absence of access to the…
Discrete empirical interpolation in the tensor t-product framework
Sridhar Chellappa, Lihong Feng, Peter Benner
The discrete empirical interpolation method (DEIM) is a well-established approach, widely used for state reconstruction using sparse sensor/measurement data, nonlinear model reduct…
Fast and Reliable Reduced-Order Models for Cardiac Electrophysiology
Sridhar Chellappa, Barış Cansız, Lihong Feng +2
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…
Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure
Sridhar Chellappa, Lihong Feng, Peter Benner
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…
Parametric Dynamic Mode Decomposition for nonlinear parametric dynamical systems
Shuwen Sun, Lihong Feng, Hoon Seng Chan +4
A non-intrusive model order reduction (MOR) method that combines features of the dynamic mode decomposition (DMD) and the radial basis function (RBF) network is proposed to predict…