Search Results for author: Tom Peterka

Found 5 papers, 2 papers with code

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

no code implementations1 Aug 2019 Wenbin He, Junpeng Wang, Hanqi Guo, Ko-Chih Wang, Han-Wei Shen, Mukund Raj, Youssef S. G. Nashed, Tom Peterka

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ.

Image Generation

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 Reinforcement Learning (RL)

Adaptive Regularization of B-Spline Models for Scientific Data

no code implementations23 Mar 2022 David Lenz, Raine Yeh, Vijay Mahadevan, Iulian Grindeanu, Tom Peterka

B-spline models are a powerful way to represent scientific data sets with a functional approximation.

Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization

1 code implementation16 Jul 2023 Skylar Wolfgang Wurster, Tianyu Xiong, Han-Wei Shen, Hanqi Guo, Tom Peterka

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.

Data Visualization

Neural Stream Functions

1 code implementation16 Jul 2023 Skylar Wolfgang Wurster, Hanqi Guo, Tom Peterka, Han-Wei Shen

Our approach takes a vector field as input and trains an implicit neural representation to learn a stream function for that vector field.

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