Flow reconstruction in time-varying geometries using graph neural networks

• URL: http://arxiv.org/abs/2411.08764v1
• Date: Wed, 13 Nov 2024 16:49:56 GMT
• Title: Flow reconstruction in time-varying geometries using graph neural networks
• Authors: Bogdan A. Danciu, Vito A. Pagone, Benjamin Böhm, Marius Schmidt, Christos E. Frouzakis,
• Abstract summary: The model incorporates a feature propagation algorithm as a preprocessing step to handle extremely sparse inputs. A binary indicator is introduced as a validity mask to distinguish between the original and propagated data points. The model is trained on a unique data set of Direct Numerical Simulations (DNS) of a motored engine at a technically relevant operating condition.
• Score: 1.0485739694839669
• License:
• Abstract: The paper presents a Graph Attention Convolutional Network (GACN) for flow reconstruction from very sparse data in time-varying geometries. The model incorporates a feature propagation algorithm as a preprocessing step to handle extremely sparse inputs, leveraging information from neighboring nodes to initialize missing features. In addition, a binary indicator is introduced as a validity mask to distinguish between the original and propagated data points, enabling more effective learning from sparse inputs. Trained on a unique data set of Direct Numerical Simulations (DNS) of a motored engine at a technically relevant operating condition, the GACN shows robust performance across different resolutions and domain sizes and can effectively handle unstructured data and variable input sizes. The model is tested on previously unseen DNS data as well as on an experimental data set from Particle Image Velocimetry (PIV) measurements that were not considered during training. A comparative analysis shows that the GACN consistently outperforms both a conventional Convolutional Neural Network (CNN) and cubic interpolation methods on the DNS and PIV test sets by achieving lower reconstruction errors and better capturing fine-scale turbulent structures. In particular, the GACN effectively reconstructs flow fields from domains up to 14 times larger than those observed during training, with the performance advantage increasing for larger domains.

Related papers

• NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability [0.0]
  This paper presents neural networks for network intrusion detection systems (NIDS) that operate on flow data preprocessed with a time window. It requires only eleven features which do not rely on deep packet inspection and can be found in most NIDS datasets and easily obtained from conventional flow collectors. The reported training accuracy exceeds 99% for the proposed method with as little as twenty neural network input features.
  arXiv  Detail & Related papers  (2024-10-24T11:36:19Z)
• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• Identification of vortex in unstructured mesh with graph neural networks [0.0]
  We present a Graph Neural Network (GNN) based model with U-Net architecture to identify the vortex in CFD results on unstructured meshes. A vortex auto-labeling method is proposed to label vortex regions in 2D CFD meshes.
  arXiv  Detail & Related papers  (2023-11-11T12:10:16Z)
• Efficient Heterogeneous Graph Learning via Random Projection [58.4138636866903]
  Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Recent pre-computation-based HGNNs use one-time message passing to transform a heterogeneous graph into regular-shaped tensors. We propose a hybrid pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN)
  arXiv  Detail & Related papers  (2023-10-23T01:25:44Z)
• FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution [4.577685231084759]
  This paper proposes a Feature-Enhanced Neural Implicit Representation (FFEINR) for super-resolution of flow field data. It can take full advantage of the implicit neural representation in terms of model structure and sampling resolution. The training process of FFEINR is facilitated by introducing feature enhancements for the input layer.
  arXiv  Detail & Related papers  (2023-08-24T02:28:18Z)
• Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data [0.0]
  This paper introduces a novel NN architecture which hybridises the Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network. The proposed method uses an unsupervised learning technique to overcome the issues with finding large volumes of labelled training data.
  arXiv  Detail & Related papers  (2022-02-11T10:33:53Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach [1.5469452301122177]
  This paper presents a dedicated Deep Neural Network architecture that reconstructs space-time traffic speeds on freeways given sparse data. A large set of empirical Floating-Car Data (FCD) collected on German freeway A9 during two months is utilized. The results show that the DNN is able to apply learned patterns, and reconstructs moving as well as stationary congested traffic with high accuracy.
  arXiv  Detail & Related papers  (2021-04-19T23:32:12Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries [62.28265459308354]
  Network learns end-to-end mapping between spatial positions and CFD quantities. Incompress laminar steady flow past a cylinder with various shapes for its cross section is considered. Network predicts the flow fields hundreds of times faster than our conventional CFD.
  arXiv  Detail & Related papers  (2020-10-15T12:15:02Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.