Search Results for author: Jiayi Xu

Found 5 papers, 2 papers with code

IDLat: An Importance-Driven Latent Generation Method for Scientific Data

no code implementations5 Aug 2022 Jingyi Shen, Haoyu Li, Jiayi Xu, Ayan Biswas, Han-Wei Shen

We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.

Data Visualization

VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations

1 code implementation25 Jul 2022 Neng Shi, Jiayi Xu, Haoyu Li, Hanqi Guo, Jonathan Woodring, Han-Wei Shen

In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space.

GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

1 code implementation18 Feb 2022 Neng Shi, Jiayi Xu, Skylar W. Wurster, Hanqi Guo, Jonathan Woodring, Luke P. Van Roekel, Han-Wei Shen

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

no code implementations13 Sep 2021 Jiayi Xu, Hanqi Guo, Han-Wei Shen, Mukund Raj, Skylar W. Wurster, Tom Peterka

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

no code implementations30 May 2021 Skylar W. Wurster, Hanqi Guo, Han-Wei Shen, Thomas Peterka, Jiayi Xu

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.


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