5 papers · 1 filter
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…