On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows
In the literature on nonlinear projection-based model order reduction for computational fluid
dynamics problems, it is often claimed that due to modal truncation, a projection-based …
dynamics problems, it is often claimed that due to modal truncation, a projection-based …
A bayesian nonlinear reduced order modeling using variational autoencoders
This paper presents a new nonlinear projection based model reduction using convolutional
Variational AutoEncoders (VAEs). This framework is applied on transient incompressible …
Variational AutoEncoders (VAEs). This framework is applied on transient incompressible …
Closure learning for nonlinear model reduction using deep residual neural network
Developing accurate, efficient, and robust closure models is essential in the construction of
reduced order models (ROMs) for realistic nonlinear systems, which generally require …
reduced order models (ROMs) for realistic nonlinear systems, which generally require …
A hyper-reduction computational method for accelerated modeling of thermal cycling-induced plastic deformations
For materials under cyclic thermal loadings, temperature and strain rate-dependent creep
deformation can occur due to the thermal expansion mismatch near material interfaces …
deformation can occur due to the thermal expansion mismatch near material interfaces …
Finite strain homogenization using a reduced basis and efficient sampling
The computational homogenization of hyperelastic solids in the geometrically nonlinear
context has yet to be treated with sufficient efficiency in order to allow for real-world …
context has yet to be treated with sufficient efficiency in order to allow for real-world …
Structure-preserving hyper-reduction and temporal localization for reduced order models of incompressible flows
RB Klein, B Sanderse - arXiv preprint arXiv:2304.09229, 2023 - arxiv.org
A novel hyper-reduction method is proposed that conserves kinetic energy and momentum
for reduced order models of the incompressible Navier-Stokes equations. The main …
for reduced order models of the incompressible Navier-Stokes equations. The main …
Augmented reduced order models for turbulence
The authors introduce an augmented-basis method (ABM) to stabilize reduced-order models
(ROMs) of turbulent incompressible flows. The method begins with standard basis functions …
(ROMs) of turbulent incompressible flows. The method begins with standard basis functions …
An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems
In this work, we propose a new method to fill the gap within an incomplete turbulent and
incompressible data field in such a way to satisfy the topological and intensity changes of …
incompressible data field in such a way to satisfy the topological and intensity changes of …
Data-targeted prior distribution for variational autoencoder
Bayesian methods were studied in this paper using deep neural networks. We are interested
in variational autoencoders, where an encoder approaches the true posterior and the …
in variational autoencoders, where an encoder approaches the true posterior and the …
[HTML][HTML] Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations
RB Klein, B Sanderse - Journal of Computational Physics, 2024 - Elsevier
A novel hyper-reduction method is proposed that conserves kinetic energy and momentum
for reduced order models of the incompressible Navier-Stokes equations. The main …
for reduced order models of the incompressible Navier-Stokes equations. The main …