Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
K Lee, KT Carlberg - Journal of Computational Physics, 2020 - Elsevier
Nearly all model-reduction techniques project the governing equations onto a linear
subspace of the original state space. Such subspaces are typically computed using methods …
subspace of the original state space. Such subspaces are typically computed using methods …
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate
physical simulations in which the intrinsic solution space falls into a subspace with a small …
physical simulations in which the intrinsic solution space falls into a subspace with a small …
Reduced basis methods for time-dependent problems
Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …
study of real-world phenomena in applied science and engineering. Computational methods …
Data-driven reduced-order models via regularised operator inference for a single-injector combustion process
This paper derives predictive reduced-order models for rocket engine combustion dynamics
via Operator Inference, a scientific machine learning approach that blends data-driven …
via Operator Inference, a scientific machine learning approach that blends data-driven …
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
Here we employ and adapt the image-to-image translation concept based on conditional
generative adversarial networks (cGAN) for learning a forward and an inverse solution …
generative adversarial networks (cGAN) for learning a forward and an inverse solution …
Lasdi: Parametric latent space dynamics identification
Enabling fast and accurate physical simulations with data has become an important area of
computational physics to aid in inverse problems, design-optimization, uncertainty …
computational physics to aid in inverse problems, design-optimization, uncertainty …
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to
many engineering applications (eg, the process of CO 2 sequestration). Here, we extend …
many engineering applications (eg, the process of CO 2 sequestration). Here, we extend …
A fast and accurate domain decomposition nonlinear manifold reduced order model
AN Diaz, Y Choi, M Heinkenschloss - Computer Methods in Applied …, 2024 - Elsevier
This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain
decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear …
decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear …
Predictive reduced order modeling of chaotic multi-scale problems using adaptively sampled projections
C Huang, K Duraisamy - Journal of Computational Physics, 2023 - Elsevier
An adaptive projection-based reduced-order model (ROM) formulation is presented for
model-order reduction of problems featuring chaotic and convection-dominant physics. An …
model-order reduction of problems featuring chaotic and convection-dominant physics. An …
Reduced order models for Lagrangian hydrodynamics
As a mathematical model of high-speed flow and shock wave propagation in a complex
multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes …
multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes …