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Found 4 papers, 1 papers with code
GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations
Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently.
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.
Deep Hierarchical Super Resolution for Scientific Data
We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform grid with minimal seam artifacts on octree node boundaries.
Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment
Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images.
