Reduced-order modeling
In recent years, reduced-order modeling techniques have proven to be powerful tools for
various problems in circuit simulation. For example, today, reduction techniques are …
various problems in circuit simulation. For example, today, reduction techniques are …
[图书][B] Advanced reduced order methods and applications in computational fluid dynamics
Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …
science and engineering, motivated by several reasons, of which we mention just a few …
Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters
In this work the development of a machine learning-based Reduced Order Model (ROM) for
the investigation of hemodynamics in a patient-specific configuration of Coronary Artery …
the investigation of hemodynamics in a patient-specific configuration of Coronary Artery …
A comparison of data-driven reduced order models for the simulation of mesoscale atmospheric flow
The simulation of atmospheric flows by means of traditional discretization methods remains
computationally intensive, hindering the achievement of high forecasting accuracy in short …
computationally intensive, hindering the achievement of high forecasting accuracy in short …
A non-intrusive data-driven reduced order model for parametrized CFD-DEM numerical simulations
The investigation of fluid-solid systems is very important in a lot of industrial processes. From
a computational point of view, the simulation of such systems is very expensive, especially …
a computational point of view, the simulation of such systems is very expensive, especially …
Neural-network learning of SPOD latent dynamics
We aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary
systems using model order reduction via the spectral proper orthogonal decomposition …
systems using model order reduction via the spectral proper orthogonal decomposition …
On the accuracy and efficiency of reduced order models: Towards real-world applications
This chapter provides an extended overview about Reduced Order Models (ROMs), with a
focus on their features in terms of efficiency and accuracy. In particular, the aim is to browse …
focus on their features in terms of efficiency and accuracy. In particular, the aim is to browse …
POD–ANN as digital twins for surge line thermal stratification
Y Yang, X Zhao, Q Cheng, R Guo, M Li… - Nuclear Engineering and …, 2024 - Elsevier
This paper describes a hybrid proper orthogonal decomposition (POD) and artificial neural
network (ANN) strategy to construct digital twins of a pressurizer surge line under thermal …
network (ANN) strategy to construct digital twins of a pressurizer surge line under thermal …
A data-driven reduced order method for parametric optimal blood flow control: application to coronary bypass graft
We consider an optimal flow control problem in a patient-specific coronary artery bypass
graft with the aim of matching the blood flow velocity with given measurements as the …
graft with the aim of matching the blood flow velocity with given measurements as the …
Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural networks
In this work, a non-intrusive data-driven ROM based on a POD–ANN approach is developed
for fast and reliable numerical simulation of blood flow patterns occurring in a patient …
for fast and reliable numerical simulation of blood flow patterns occurring in a patient …