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 …
An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
K Balla, R Sevilla, O Hassan, K Morgan - Applied Mathematical Modelling, 2021 - Elsevier
This work proposes a novel multi-output neural network for the prediction of aerodynamic
coefficients of aerofoils in two dimensions and wings in three dimensions. Contrary to …
coefficients of aerofoils in two dimensions and wings in three dimensions. Contrary to …
[HTML][HTML] An overlapping domain decomposition method for the solution of parametric elliptic problems via proper generalized decomposition
A non-intrusive proper generalized decomposition (PGD) strategy, coupled with an
overlapping domain decomposition (DD) method, is proposed to efficiently construct …
overlapping domain decomposition (DD) method, is proposed to efficiently construct …
A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces
Reduced order modeling (ROM) provides an efficient framework to compute solutions of
parametric problems. Basically, it exploits a set of precomputed high-fidelity solutions …
parametric problems. Basically, it exploits a set of precomputed high-fidelity solutions …
Multiparametric modeling of composite materials based on non-intrusive PGD informed by multiscale analyses: Application for real-time stiffness prediction of woven …
In this paper, a multiparametric solution of the stiffness properties of woven composites
involving several microstructure parameters is performed. For this purpose, non-intrusive …
involving several microstructure parameters is performed. For this purpose, non-intrusive …
[HTML][HTML] Model Order Reduction Methods for Rotating Electrical Machines: A Review
KL Kiss, T Orosz - Energies, 2024 - mdpi.com
Due to the rise of e-mobility applications, there is an increased demand to create more
accurate control methods, which can reduce the loss in an e-drive system. The accurate …
accurate control methods, which can reduce the loss in an e-drive system. The accurate …
A nonintrusive distributed reduced‐order modeling framework for nonlinear structural mechanics—Application to elastoviscoplastic computations
In this work, we propose a framework that constructs reduced‐order models for nonlinear
structural mechanics in a nonintrusive fashion and can handle large‐scale simulations …
structural mechanics in a nonintrusive fashion and can handle large‐scale simulations …
Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using POD-based reduced-order modeling
This study explores reduced-order modeling for analyzing time-dependent diffusion-
deformation of hydrogels. The full-order model describing hydrogel transient behavior …
deformation of hydrogels. The full-order model describing hydrogel transient behavior …
Efficient uncertainty quantification of stochastic problems in CFD by combination of compressed sensing and POD-Kriging
Q Lu, L Wang, L Li - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
This paper proposes an uncertainty quantification method that combines compressed
sensing and POD-Kriging that inherits the benefits of each key element. The compressed …
sensing and POD-Kriging that inherits the benefits of each key element. The compressed …
Reduced order modeling for physically-based augmented reality
In this work we explore the possibilities of reduced order modeling for augmented reality
applications. We consider parametric reduced order models based upon separate (affine) …
applications. We consider parametric reduced order models based upon separate (affine) …