Model identification of reduced order fluid dynamics systems using deep learning
This paper presents a novel model reduction method: deep learning reduced order model,
which is based on proper orthogonal decomposition and deep learning methods. The deep …
which is based on proper orthogonal decomposition and deep learning methods. The deep …
Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition
Because of its nonlinearity and path-dependency, analysis of the elasto-plastic behavior of
the finite element (FE) model is computationally expensive. By directly learning sequential …
the finite element (FE) model is computationally expensive. By directly learning sequential …
A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications
A novel parameterized non-intrusive reduced order model (P-NIROM) based on proper
orthogonal decomposition (POD) has been developed. This P-NIROM is a generic and …
orthogonal decomposition (POD) has been developed. This P-NIROM is a generic and …
Domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) nonlinear model reduction
A novel domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) model-reduction
method applicable to parameterized systems of nonlinear algebraic equations (eg, arising …
method applicable to parameterized systems of nonlinear algebraic equations (eg, arising …
A one-shot overlapping Schwarz method for component-based model reduction: application to nonlinear elasticity
We propose a component-based (CB) parametric model order reduction (pMOR) formulation
for parameterized nonlinear elliptic partial differential equations (PDEs) based on …
for parameterized nonlinear elliptic partial differential equations (PDEs) based on …
[HTML][HTML] Development of a reduced-order model for large-scale Eulerian–Lagrangian simulations
S Li, G Duan, M Sakai - Advanced Powder Technology, 2022 - Elsevier
Multiphase flows with solid particles are commonly encountered in various industries. The
CFD–DEM method is extensively used to simulate their dynamical behavior. However, the …
CFD–DEM method is extensively used to simulate their dynamical behavior. However, the …
The Schwarz alternating method for the seamless coupling of nonlinear reduced order models and full order models
Projection-based model order reduction allows for the parsimonious representation of full
order models (FOMs), typically obtained through the discretization of certain partial …
order models (FOMs), typically obtained through the discretization of certain partial …
Application of proper generalized decomposition to multigroup neutron diffusion eigenvalue calculations
ZM Prince, JC Ragusa - Progress in Nuclear Energy, 2020 - Elsevier
In this paper, proper generalized decomposition (PGD) is utilized to reduce the
computational burden of evaluating multigroup neutron diffusion eigenvalue problems. PGD …
computational burden of evaluating multigroup neutron diffusion eigenvalue problems. PGD …
A non-overlapping optimization-based domain decomposition approach to component-based model reduction of incompressible flows
We present a component-based model order reduction procedure to efficiently and
accurately solve parameterized incompressible flows governed by the Navier-Stokes …
accurately solve parameterized incompressible flows governed by the Navier-Stokes …
Explicit synchronous partitioned scheme for coupled reduced order models based on composite reduced bases
This paper formulates, analyzes and demonstrates numerically a method for the explicit
partitioned solution of coupled interface problems involving combinations of projection …
partitioned solution of coupled interface problems involving combinations of projection …