18 citations · 44 across the 8 of their papers we have counts for
8 papers
Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields
Tianyu Xiong, Skylar Wurster, Han-Wei Shen
Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional f…
Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network
Tianyu Xiong, Skylar W. Wurster, Hanqi Guo +2
Feature grid Scene Representation Networks (SRNs) have been applied to scientific data as compact functional surrogates for analysis and visualization. As SRNs are black-box lossy…
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
Skylar Wolfgang Wurster, Tianyu Xiong, Han-Wei Shen +2
Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of…
Neural Stream Functions
Skylar Wolfgang Wurster, Hanqi Guo, Tom Peterka +1
We present a neural network approach to compute stream functions, which are scalar functions with gradients orthogonal to a given vector field. As a result, isosurfaces of the stre…
GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations
Neng Shi, Jiayi Xu, Skylar W. Wurster +4
We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for dom…
Reinforcement Learning for Load-balanced Parallel Particle Tracing
Jiayi Xu, Hanqi Guo, Han-Wei Shen +3
We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three nov…
Deep Hierarchical Super Resolution for Scientific Data
Skylar W. Wurster, Hanqi Guo, Han-Wei Shen +2
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-r…
Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment
Skylar W. Wurster, Arkadiusz Sitek, Jian Chen +3
Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images. In this work, we combine these radiologi…