A non-intrusive neural network model order reduction algorithm for parameterized parabolic PDEs

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

(2021) Stefania Fresca et al.   Frontiers in Physiology

Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks

(2021) M. Salvador et al.   COMPUTERS & MATHEMATICS WITH APPLICATIONS

Deep neural networks for nonlinear model order reduction of unsteady flows

(2020) Hamidreza Eivazi et al.   PHYSICS OF FLUIDS

Deep learning-based reduced order models in cardiac electrophysiology

(2020) Stefania Fresca et al.   PLoS One

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

(2019) Qian Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Prediction of aerodynamic flow fields using convolutional neural networks

(2019) Saakaar Bhatnagar et al.   COMPUTATIONAL MECHANICS

Reduced order modeling for nonlinear structural analysis using Gaussian process regression

(2018) Mengwu Guo et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Non-intrusive reduced order modeling of nonlinear problems using neural networks

(2018) J.S. Hesthaven et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven reduced order modeling for time-dependent problems

(2018) Mengwu Guo et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Nonlinear Model Reduction via Discrete Empirical Interpolation

(2010) Saifon Chaturantabut et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING