11 papers
Direct Topology Tracking in Continuous Implicit Models
Guanqun Ma, David Lenz, Kaiyuan Tang +4
We present a framework for tracking topological features directly within continuous implicit models. Such models, including implicit neural representations (INRs) and multivariate…
A Physics-Informed B-Spline Framework for Continuous Approximation of Flow Data
Junoh Jung, David Lenz, Emil Constantinescu +1
Continuous approximations of flow data are useful for downstream analysis, differentiation, and visualization, but purely data-driven reconstructions do not, in general, preserve t…
Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields
Sophia Zorek, Kushal Vyas, Yuhao Liu +3
Neural fields, also known as implicit neural representations (INRs), offer a powerful framework for modeling continuous geometry, but their effectiveness in high-dimensional scient…
SASAV: Self-Directed Agent for Scientific Analysis and Visualization
Jianxin Sun, David Lenz, Tom Peterka +1
With recent advances in frontier multimodal large language models (MLLMs) for data understanding and visual reasoning, the role of LLMs has evolved from passive LLM-as-an-interface…
SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents
Kuangshi Ai, Haichao Miao, Kaiyuan Tang +13
Recent advances in large language models (LLMs) have enabled agentic systems to translate natural-language intent into executable scientific visualization (SciVis) tasks. Despite r…
Extracting Complex Topology from Multivariate Functional Approximation: Contours, Jacobi Sets, and Ridge-Valley Graphs
Guanqun Ma, David Lenz, Hanqi Guo +2
Implicit continuous models, such as functional models and implicit neural networks, are an increasingly popular method for replacing discrete data representations with continuous,…
F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding
Jianxin Sun, David Lenz, Hongfeng Yu +1
Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete…
Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks
Jianxin Sun, David Lenz, Hongfeng Yu +1
Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image in…
Adaptive Multi-Resolution Encoding for Interactive Large-Scale Volume Visualization through Functional Approximation
Jianxin Sun, David Lenz, Hongfeng Yu +1
Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Vol…
Critical Point Extraction from Multivariate Functional Approximation
Guanqun Ma, David Lenz, Tom Peterka +2
Advances in high-performance computing require new ways to represent large-scale scientific data to support data storage, data transfers, and data analysis within scientific workfl…
Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation
Jianxin Sun, David Lenz, Hongfeng Yu +1
Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analy…