74 citations · 157 across the 9 of their papers we have counts for
9 papers
Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators
Zhe Bai, Liqian Peng
Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad a…
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
Kevin T. Carlberg, Antony Jameson, Mykel J. Kochenderfer +3
Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, ye…
Structure-preserving model reduction for marginally stable LTI systems
Liqian Peng, Kevin Carlberg
This work proposes a structure-preserving model reduction method for marginally stable linear time-invariant (LTI) systems. In contrast to Lyapunov-stability-based approaches---whi…
Structure-Preserving Model Reduction of Forced Hamiltonian Systems
Liqian Peng, Kamran Mohseni
This paper reports a development in the proper symplectic decomposition (PSD) for model reduction of forced Hamiltonian systems. As an analogy to the proper orthogonal decompositio…
Symplectic Model Reduction of Hamiltonian Systems
Liqian Peng, Kamran Mohseni
In this paper, a symplectic model reduction technique, proper symplectic decomposition (PSD) with symplectic Galerkin projection, is proposed to save the computational cost for the…
Nonlinear Model Reduction via an Adaptive Weighting of Snapshots
Liqian Peng, Kamran Mohseni
In this paper, we propose a new approach to model reduction of parameterized partial differential equations (PDEs) based on the concept of adaptive reduced bases. The presented app…