Flow Field Reduction Via Reconstructing Vector Data From 3-D Streamlines Using Deep Learning
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- Type
- article
- Published
- 2019-07-01
- Cited by
- 41
- References
- 16
- OpenAlex
- https://openalex.org/W31226060
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53598773
Keywords
Political science
References
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Deep Networks for Image Super-Resolution with Sparse Prior
- From basis functions to basis fields: vector field approximation from sparse data
- Fixed-Rate Compressed Floating-Point Arrays
- Vector field reconstruction from sparse samples with applications
- Gradient vector flow: a new external force for snakes
- Similarity-Guided Streamline Placement with Error Evaluation
- Deep Residual Learning for Image Recognition
- Context Encoders: Feature Learning by Inpainting
- Fast Error-Bounded Lossy HPC Data Compression with SZ
- High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis
- Globally and locally consistent image completion
- Data Reduction Techniques for Simulation, Visualization and Data Analysis
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- Adam: A Method for Stochastic Optimization
- Ieee Transactions on Visualization and Computer Graphics 1 a Unified Approach to Streamline Selection and Viewpoint Selection for 3d Flow Visualization
Cited by
- TSR-TVD: Temporal Super-Resolution for Time-Varying Data Analysis and Visualization
- SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization
- Learning Adaptive Sampling and Reconstruction for Volume Visualization
- A Survey of Seed Placement and Streamline Selection Techniques
- A comparative study of convolutional neural network models for wind field downscaling
- Visualization Laboratory at University of Notre Dame
- A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation
- V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data
- PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- Vortex Boundary Identification using Convolutional Neural Network
- Reconstructing Unsteady Flow Data From Representative Streamlines via Diffusion and Deep-Learning-Based Denoising
- Compressive Neural Representations of Volumetric Scalar Fields
- Deep Regression Network-Assisted Efficient Streamline Generation Method
- STNet: An End-to-End Generative Framework for Synthesizing Spatiotemporal Super-Resolution Volumes
- Integration-Aware Vector Field Super Resolution
- Exploratory Lagrangian-Based Particle Tracing Using Deep Learning
- TSR-VFD: Generating temporal super-resolution for unsteady vector field data
- Numerical Flow Visualization: Vista and Expedition
- Application of boundary-fitted convolutional neural network to simulate non-Newtonian fluid flow behavior in eccentric annulus
- DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
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