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.

reinforcement-learning

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.

Super-Resolution

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