16 citations · 16 across the 7 of their papers we have counts for
7 papers
Customizable Adaptive Regularization Techniques for B-Spline Modeling
David Lenz, Raine Yeh, Vijay Mahadevan +2
B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to b…
Parallel Domain Decomposition techniques applied to Multivariate Functional Approximation of discrete data
Vijay S. Mahadevan, David Lenz, Iulian Grindeanu +1
Compactly expressing large-scale datasets through Multivariate Functional Approximations (MFA) can be critically important for analysis and visualization to drive scientific discov…
MFA-DVR: Direct Volume Rendering of MFA Models
Jianxin Sun, David Lenz, Hongfeng Yu +1
3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumet…
Adaptive Regularization of B-Spline Models for Scientific Data
David Lenz, Raine Yeh, Vijay Mahadevan +2
B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to b…
Construction of 4D Simplex Space-Time Meshes for Local Bisection Schemes
David Lenz
Adaptive mesh refinement is a key component of efficient unstructured space-time finite element methods. Underlying any adaptive mesh refinement scheme is, of course, a method for…
Fourier-Informed Knot Placement Schemes for B-Spline Approximation
David Lenz, Oana Marin, Vijay Mahadevan +2
Fitting B-splines to discrete data is especially challenging when the given data contain noise, jumps, or corners. Here, we describe how periodic data sets with these features can…