GS-Surrogate creates a canonical Gaussian field that is sequentially deformed by simulation parameters to enable real-time, controllable 3D exploration of ensemble data while separating simulation variations from visualization adjustments.
An Image-Based Approach to Extreme Scale in Situ Visualization and Analysis
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A pointwise multivariate information-driven sampling method generates reduced datasets that preserve statistical associations among variables for effective feature queries and analysis.
citing papers explorer
-
GS-Surrogate: Deformable Gaussian Splatting for Parameter Space Exploration of Ensemble Simulations
GS-Surrogate creates a canonical Gaussian field that is sequentially deformed by simulation parameters to enable real-time, controllable 3D exploration of ensemble data while separating simulation variations from visualization adjustments.
-
Multivariate Pointwise Information-Driven Data Sampling and Visualization
A pointwise multivariate information-driven sampling method generates reduced datasets that preserve statistical associations among variables for effective feature queries and analysis.