S Fresca, A Manzoni - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models (ROMs)–built …
We develop a general framework for data-driven approximation of input-output maps between infinitedimensional spaces. The proposed approach is motivated by the recent …
Computational fluid dynamics (CFD) models have been used for the simulation of urban airflow and pollutant dispersion, due to their capability to capture different length scales and …
Reduced order modeling (ROM) has been widely used to create lower order, computationally inexpensive representations of higher-order dynamical systems. Using …
The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …
Highly accurate numerical or physical experiments are often very time-consuming or expensive to obtain. When time or budget restrictions prohibit the generation of additional …
WD Fries, X He, Y Choi - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty …
When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for …
Inspired by our previous work on a quadratic approximation manifold [1], we propose in this paper a computationally tractable approach for combining a projection-based reduced-order …