A Short Review on Data Modelling for Vector Fields

• URL: http://arxiv.org/abs/2009.00577v1
• Date: Tue, 1 Sep 2020 17:07:29 GMT
• Title: A Short Review on Data Modelling for Vector Fields
• Authors: Jun Li, Wanrong Hong, Yusheng Xiang
• Abstract summary: Machine learning methods have proven highly successful in dealing with a wide variety of data analysis and analytics tasks. The recent success of end-to-end modelling scheme using deep neural networks allows the extension to more sophisticated and structured practical data. This review article is dedicated to recent computational tools of vector fields, including vector data representations, predictive model of spatial data, as well as applications in computer vision, signal processing, and empirical sciences.
• Score: 5.51641435875237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning methods based on statistical principles have proven highly successful in dealing with a wide variety of data analysis and analytics tasks. Traditional data models are mostly concerned with independent identically distributed data. The recent success of end-to-end modelling scheme using deep neural networks equipped with effective structures such as convolutional layers or skip connections allows the extension to more sophisticated and structured practical data, such as natural language, images, videos, etc. On the application side, vector fields are an extremely useful type of data in empirical sciences, as well as signal processing, e.g. non-parametric transformations of 3D point clouds using 3D vector fields, the modelling of the fluid flow in earth science, and the modelling of physical fields. This review article is dedicated to recent computational tools of vector fields, including vector data representations, predictive model of spatial data, as well as applications in computer vision, signal processing, and empirical sciences.

Related papers

• An Intrinsic Vector Heat Network [64.55434397799728]
  This paper introduces a novel neural network architecture for learning tangent vector fields embedded in 3D. We introduce a trainable vector heat diffusion module to spatially propagate vector-valued feature data across the surface. We also demonstrate the effectiveness of our method on the useful industrial application of quadrilateral mesh generation.
  arXiv  Detail & Related papers  (2024-06-14T00:40:31Z)
• Synthetic Data Generation and Deep Learning for the Topological Analysis of 3D Data [0.0]
  This research uses deep learning to estimate the topology of sparse, unordered point cloud scenes in 3D. The experimental results of this pilot study support the hypothesis that, with the aid of sophisticated synthetic data generation, neural networks can perform segmentation-based topological data analysis.
  arXiv  Detail & Related papers  (2023-09-29T04:37:35Z)
• 3D Adversarial Augmentations for Robust Out-of-Domain Predictions [115.74319739738571]
  We focus on improving the generalization to out-of-domain data. We learn a set of vectors that deform the objects in an adversarial fashion. We perform adversarial augmentation by applying the learned sample-independent vectors to the available objects when training a model.
  arXiv  Detail & Related papers  (2023-08-29T17:58:55Z)
• CHALLENGER: Training with Attribution Maps [63.736435657236505]
  We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. In particular, we show that our generic domain-independent approach yields state-of-the-art results in vision, natural language processing and on time series tasks.
  arXiv  Detail & Related papers  (2022-05-30T13:34:46Z)
• Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas [0.41998444721319217]
  This work rephrases the problem from computing 3D flow fields using CFD to a 2D image-to-image translation-based problem on the building footprints to predict the flow field at pedestrian height level. We investigate the use of generative adversarial networks (GAN), such as Pix2Pix and CycleGAN representing state-of-the-art for image-to-image translation task in various domains.
  arXiv  Detail & Related papers  (2021-12-15T19:37:11Z)
• Equivariant vector field network for many-body system modeling [65.22203086172019]
  Equivariant Vector Field Network (EVFN) is built on a novel equivariant basis and the associated scalarization and vectorization layers. We evaluate our method on predicting trajectories of simulated Newton mechanics systems with both full and partially observed data.
  arXiv  Detail & Related papers  (2021-10-26T14:26:25Z)
• Data-Driven Shadowgraph Simulation of a 3D Object [50.591267188664666]
  We are replacing the numerical code by a computationally cheaper projection based surrogate model. The model is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This model has shown a good quality reconstruction in a problem of perturbation of data within a narrow range of simulation parameters and can be used for input data of large size.
  arXiv  Detail & Related papers  (2021-06-01T08:46:04Z)
• 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)
• PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data [7.214140640112874]
  This paper addresses the task of extracting meaningful results in an automated manner from high dimensional data sets. We propose deep learning methods which are capable of processing such data and which can be trained to solve relevant tasks on simulation data. We compile a large dataset of 2D simulations of the flow field around airfoils which contains 16000 flow fields with which we tested and compared approaches.
  arXiv  Detail & Related papers  (2021-03-11T09:28:00Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• UV-Net: Learning from Boundary Representations [17.47054752280569]
  We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. B-rep data presents some unique challenges when used with modern machine learning due to the complexity of the data structure and its support for both continuous non-Euclidean geometric entities and discrete topological entities.
  arXiv  Detail & Related papers  (2020-06-18T00:12:52Z)

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.