Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

• URL: http://arxiv.org/abs/2110.08338v1
• Date: Fri, 15 Oct 2021 19:54:32 GMT
• Title: Exploratory Lagrangian-Based Particle Tracing Using Deep Learning
• Authors: Mengjiao Han, Sudhanshu Sane, Chris R. Johnson
• Abstract summary: This paper presents a novel deep neural network-based particle tracing method to explore time-varying vector fields represented by Lagrangian flow maps. In our workflow, in situ processing is first utilized to extract Lagrangian flow maps, and deep neural networks then use the extracted data to learn flow field behavior. Our method requires a fixed memory footprint of 10.5 MB to encode a Lagrangian representation of a time-varying vector field while maintaining accuracy.
• Score: 4.486141167325432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian representations have been increasingly researched as a means to improve scientific time-varying vector field exploration capabilities. This paper presents a novel deep neural network-based particle tracing method to explore time-varying vector fields represented by Lagrangian flow maps. In our workflow, in situ processing is first utilized to extract Lagrangian flow maps, and deep neural networks then use the extracted data to learn flow field behavior. Using a trained model to predict new particle trajectories offers a fixed small memory footprint and fast inference. To demonstrate and evaluate the proposed method, we perform an in-depth study of performance using a well-known analytical data set, the Double Gyre. Our study considers two flow map extraction strategies as well as the impact of the number of training samples and integration durations on efficacy, evaluates multiple sampling options for training and testing and informs hyperparameter settings. Overall, we find our method requires a fixed memory footprint of 10.5 MB to encode a Lagrangian representation of a time-varying vector field while maintaining accuracy. For post hoc analysis, loading the trained model costs only two seconds, significantly reducing the burden of I/O when reading data for visualization. Moreover, our parallel implementation can infer one hundred locations for each of two thousand new pathlines across the entire temporal resolution in 1.3 seconds using one NVIDIA Titan RTX GPU.

Related papers

• Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization [87.21285093582446]
  Diffusion Generative Flow Samplers (DGFS) is a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments. Our method takes inspiration from the theory developed for generative flow networks (GFlowNets)
  arXiv  Detail & Related papers  (2023-10-04T09:39:05Z)
• AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources [2.682578132719034]
  We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.
  arXiv  Detail & Related papers  (2023-04-09T19:11:04Z)
• 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)
• Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining [58.10436813430554]
  Mini-batch training of graph neural networks (GNNs) requires a lot of computation and data movement. We argue in favor of performing mini-batch training with neighborhood sampling in a distributed multi-GPU environment. We present a sequence of improvements to mitigate these bottlenecks, including a performance-engineered neighborhood sampler. We also conduct an empirical analysis that supports the use of sampling for inference, showing that test accuracies are not materially compromised.
  arXiv  Detail & Related papers  (2021-10-16T02:41:35Z)
• Efficient Nearest Neighbor Language Models [114.40866461741795]
  Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore. We show how to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
  arXiv  Detail & Related papers  (2021-09-09T12:32:28Z)
• Time Varying Particle Data Feature Extraction and Tracking with Neural Networks [20.825102707056647]
  We take a deep learning approach to create feature representations for scientific particle data to assist feature extraction and tracking. We employ a deep learning model, which produces latent vectors to represent the relation between spatial locations and physical attributes in a local neighborhood. To achieve fast feature tracking, the mean-shift tracking algorithm is applied in the feature space.
  arXiv  Detail & Related papers  (2021-05-27T15:38:14Z)
• Sample and Computation Redistribution for Efficient Face Detection [137.19388513633484]
  Training data sampling and computation distribution strategies are the keys to efficient and accurate face detection. scrfdf34 outperforms the best competitor, TinaFace, by $3.86%$ (AP at hard set) while being more than emph3$times$ faster on GPUs with VGA-resolution images.
  arXiv  Detail & Related papers  (2021-05-10T23:51:14Z)
• Neural Particle Image Velocimetry [4.416484585765027]
  We introduce a convolutional neural network adapted to the problem, namely Volumetric Correspondence Network (VCN) The network is thoroughly trained and tested on a dataset containing both synthetic and real flow data. Our analysis indicates that the proposed approach provides improved efficiency also keeping accuracy on par with other state-of-the-art methods in the field.
  arXiv  Detail & Related papers  (2021-01-28T12:03:39Z)
• 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)
• Faster object tracking pipeline for real time tracking [0.0]
  Multi-object tracking (MOT) is a challenging practical problem for vision based applications. This paper showcases a generic pipeline which can be used to speed up detection based object tracking methods.
  arXiv  Detail & Related papers  (2020-11-08T06:33:48Z)
• Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset [4.4074213830420055]
  This research applies a test-proven deep learning architecture to predict the performance under various restrictions. The major challenge is the vast amount of data points Deep Neural Network (DNN) demands, which is improvident to simulate. The final stage, a user interface, made the model capable of optimizing with given user input of minimum area and viscosity.
  arXiv  Detail & Related papers  (2020-01-18T07:35:00Z)

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.