4 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…
Metrics for Parametric Families of Networks
Mario Gómez, Guanqun Ma, Tom Needham +1
We introduce a general framework for analyzing data modeled as parameterized families of networks. Building on a Gromov-Wasserstein variant of optimal transport, we define a family…
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,…
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…