23 citations · 102 across the 25 of their papers we have counts for
26 papers
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-…
Surrogate modeling for convection-dominated parametric problems based on error learning
Lihong Feng, Ali Hassan, Shuwen Sun
Convection-dominated problems are known for their slow Kolmogorov -width decays and are challenging for model order reduction (MOR). In this work, we propose a hybrid surrogate…
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…
Spatially-Enhanced Temporal Fusion Transformer: Interpretable Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs
Shuwen Sun, Lihong Feng, Peter Benner
We explore the promising performance of a transformer model in predicting outputs of parametric dynamical systems with external time-varying input signals. The outputs of such syst…
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…
Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models
Shuwen Sun, Lihong Feng, Peter Benner
Numerically solving a large parametric nonlinear dynamical system is challenging due to its high complexity and the high computational costs. In recent years, machine-learning-aide…
Estimates of the Kolmogorov n-width for nonlinear transformations with application to distributed-parameter control systems
Alexander Zuyev, Lihong Feng, Peter Benner
This paper aims at characterizing the approximability of bounded sets in the range of nonlinear operators in Banach spaces by finite-dimensional linear varieties. In particular, th…
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…
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems
Harshit Kapadia, Lihong Feng, Peter Benner
When repeated evaluations for varying parameter configurations of a high-fidelity physical model are required, surrogate modeling techniques based on model order reduction are desi…
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…
Multi-fidelity error estimation accelerates greedy model reduction of complex dynamical systems
Lihong Feng, Luigi Lombardi, Giulio Antonini +1
Model order reduction usually consists of two stages: the offline stage and the online stage. The offline stage is the expensive part that sometimes takes hours till the final redu…
Fast A Posteriori State Error Estimation for Reliable Frequency Sweeping in Microwave Circuits via the Reduced-Basis Method
Valentin de la Rubia, Sridhar Chellappa, Lihong Feng +1
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 previo…
Multivariate moment matching for model order reduction of quadratic-bilinear systems using error bounds
Muhammad Altaf Khattak, Mian Ilyas Ahmad, Lihong Feng +1
We propose an adaptive moment-matching framework for model order reduction of quadratic-bilinear descriptor systems. In this framework, an important issue is the selection of those…
Inf-Sup-Constant-Free State Error Estimator for Model Order Reduction of Parametric Systems in Electromagnetics
Sridhar Chellappa, Lihong Feng, Valentin de la Rubia +1
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.…
A FOM/ROM Hybrid Approach for Accelerating Numerical Simulations
Lihong Feng, Guosheng Fu, Zhu Wang
The basis generation in reduced order modeling usually requires multiple high-fidelity large-scale simulations that could take a huge computational cost. In order to accelerate the…
A Training Set Subsampling Strategy for the Reduced Basis Method
Sridhar Chellappa, Lihong Feng, Peter Benner
We present a subsampling strategy for the offline stage of the Reduced Basis Method. The approach is aimed at bringing down the considerable offline costs associated with using a f…
Reduced basis method for the nonlinear Poisson-Boltzmann equation regularized by the range-separated canonical tensor format
Cleophas Kweyu, Lihong Feng, Matthias Stein +1
The Poisson-Boltzmann equation (PBE) is a fundamental implicit solvent continuum model for calculating the electrostatic potential of large ionic solvated biomolecules. However, it…
On Error Estimation for Reduced-order Modeling of Linear Non-parametric and Parametric Systems
Lihong Feng, Peter Benner
Motivated by a recently proposed error estimator for the transfer function of the reduced-order model of a given linear dynamical system, we further develop more theoretical result…
Adaptive Interpolatory MOR by Learning the Error Estimator in the Parameter Domain
Sridhar Chellappa, Lihong Feng, Valentin de la Rubia +1
Interpolatory methods offer a powerful framework for generating reduced-order models (ROMs) for non-parametric or parametric systems with time-varying inputs. Choosing the interpol…
Implicit Higher-Order Moment Matching Technique for Model Reduction of Quadratic-bilinear Systems
Mian Muhammad Arsalan Asif, Mian Ilyas Ahmad, Peter Benner +2
We propose a projection based multi-moment matching method for model order reduction of quadratic-bilinear systems. The goal is to construct a reduced system that ensures higher-or…
Adaptive Basis Construction and Improved Error Estimation for Parametric Nonlinear Dynamical Systems
Sridhar Chellappa, Lihong Feng, Peter Benner
An adaptive scheme to generate reduced-order models for parametric nonlinear dynamical systems is proposed. It aims to automatize the POD-Greedy algorithm combined with empirical i…
An Adaptive Sampling Approach for the Reduced Basis Method
Sridhar Chellappa, Lihong Feng, Peter Benner
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 pa…
An Adaptive Pole-Matching Method for Interpolating Reduced-Order Models
Yao Yue, Lihong Feng, Peter Benner
An adaptive parametric reduced-order modeling method based on interpolating poles of reduced-order models is proposed in this paper. To guarantee correct interpolation, a pole-matc…