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Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets.
Exploratory Lagrangian-Based Particle Tracing Using Deep Learning
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.
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