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Found 5 papers, 2 papers with code
Neural Stream Functions
Our approach takes a vector field as input and trains an implicit neural representation to learn a stream function for that vector field.
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems.
Adaptive Regularization of B-Spline Models for Scientific Data
B-spline models are a powerful way to represent scientific data sets with a functional approximation.
Reinforcement Learning for Load-balanced Parallel Particle Tracing
Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations.
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ.
