Ko-Chih Wang

TL;DR: This work proposes InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ, designed as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results.

TL;DR: In this article, a neural network-based surrogate model is used for visual analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation, which can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance.

TL;DR: A novel approach called statistical super-resolution is proposed to tackle the big data problem for cosmological data analysis and visualization by applying in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage.

TL;DR: This work presents a novel ray-based representation storing ray based histograms and depth information that recovers the evolution of volume data between sampled time steps and is able to provide fast rendering in the context of transfer function exploration to support visualization of feature evolution in time-varying data.