[HTML][HTML] A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
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) …
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …
Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks
Mesh-based simulations play a key role when modeling complex physical systems that, in
many disciplines across science and engineering, require the solution to parametrized time …
many disciplines across science and engineering, require the solution to parametrized time …
Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations
Abstract Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to
simulate complex multiphysics problems with accelerated performance times. However …
simulate complex multiphysics problems with accelerated performance times. However …
Three-dimensional high-sampling super-resolution reconstruction of swirling flame based on physically consistent diffusion models
L Huang, C Zheng, Y Chen, W Xu, F Yang - Physics of Fluids, 2024 - pubs.aip.org
Three-dimensional swirling flame flow fields are often limited by factors such as system
complexity and operational difficulty, resulting in relatively low achievable spatial resolution …
complexity and operational difficulty, resulting in relatively low achievable spatial resolution …
Development of whole system digital twins for advanced reactors: Leveraging graph neural networks and SAM simulations
In this work, we introduce a novel method to develop whole system digital twins (DTs) for
advanced nuclear reactors. This method treats a complex reactor system as a …
advanced nuclear reactors. This method treats a complex reactor system as a …
An implicit gnn solver for poisson-like problems
This paper presents Ψ-GNN, a novel Graph Neural Network (GNN) approach for solving the
ubiquitous Poisson PDE problems on general unstructured meshes with mixed boundary …
ubiquitous Poisson PDE problems on general unstructured meshes with mixed boundary …
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks
A graph neural network (GNN) approach is introduced in this work which enables mesh-
based three-dimensional super-resolution of fluid flows. In this framework, the GNN is …
based three-dimensional super-resolution of fluid flows. In this framework, the GNN is …
Interpretable A-posteriori error indication for graph neural network surrogate models
Data-driven surrogate modeling has surged in capability in recent years with the emergence
of graph neural networks (GNNs), which can operate directly on mesh-based …
of graph neural networks (GNNs), which can operate directly on mesh-based …
[HTML][HTML] Multiside graph neural network-based attention for local co-occurrence features fusion in lung nodule classification
Early diagnosis of lung cancer is critical as it can save people's lives. Long-range
dependencies within volumetric medical images are essential attributes for accurate lung …
dependencies within volumetric medical images are essential attributes for accurate lung …
[HTML][HTML] Data-driven reduced order surrogate modeling for coronary in-stent restenosis
Background: The intricate process of coronary in-stent restenosis (ISR) involves the interplay
between different mediators, including platelet-derived growth factor, transforming growth …
between different mediators, including platelet-derived growth factor, transforming growth …