[HTML][HTML] POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to
overcome common limitations shared by conventional reduced order models (ROMs)–built …
overcome common limitations shared by conventional reduced order models (ROMs)–built …
[HTML][HTML] Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation,
entail a huge computational complexity when dealing with input-output maps involving the …
entail a huge computational complexity when dealing with input-output maps involving the …
[HTML][HTML] Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
Many applications in computational physics involve approximating problems with
microstructure, characterized by multiple spatial scales in their data. However, these …
microstructure, characterized by multiple spatial scales in their data. However, these …
[HTML][HTML] Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method
In order to optimally design materials, it is crucial to understand the structure–property
relations in the material by analyzing the effect of microstructure parameters on the …
relations in the material by analyzing the effect of microstructure parameters on the …
Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
Forward uncertainty quantification (UQ) for partial differential equations is a many-query task
that requires a significant number of model evaluations. The objective of this work is to …
that requires a significant number of model evaluations. The objective of this work is to …
[HTML][HTML] Investigation of physics-informed deep learning for the prediction of parametric, three-dimensional flow based on boundary data
The placement of temperature sensitive and safety-critical components is crucial in the
automotive industry. It is therefore inevitable, even at the design stage of new vehicles, that …
automotive industry. It is therefore inevitable, even at the design stage of new vehicles, that …
Digital twin temperature field prediction of laser powder bed fusion through proper orthogonal decomposition with radial basis function
Digital Twin framework, integrated with in-situ sensing and physical simulation, can
significantly improve process productivity and product quality of Laser Powder Bed Fusion …
significantly improve process productivity and product quality of Laser Powder Bed Fusion …
Conditional variational autoencoder with Gaussian process regression recognition for parametric models
X Zhang, L Jiang - Journal of Computational and Applied Mathematics, 2024 - Elsevier
In this article, we present a data-driven method for parametric models with noisy observation
data. Gaussian process regression based reduced order modeling (GPR-based ROM) can …
data. Gaussian process regression based reduced order modeling (GPR-based ROM) can …
Predicting output responses of nonlinear dynamical systems with parametrized inputs using LSTM
L Feng - IEEE Journal on Multiscale and Multiphysics …, 2023 - ieeexplore.ieee.org
Long Short-Term Memory (LSTM) has been more and more used to predict time evolution of
dynamics for many problems, especially the fluid dynamics. Usually, it is applied to the latent …
dynamics for many problems, especially the fluid dynamics. Usually, it is applied to the latent …
An efficient reduced order model for nonlinear transient porous media flow with time-varying injection rates
Abstract An intrusive Reduced Order Model (ROM) is developed for nonlinear porous media
flow problems with transient and time-discontinuous fluid injection rates. The proposed ROM …
flow problems with transient and time-discontinuous fluid injection rates. The proposed ROM …