26 citations · 59 across the 6 of their papers we have counts for
6 papers
Parallel Discrete Convolutions on Adaptive Particle Representations of Images
Joel Jonsson, Bevan L. Cheeseman, Suryanarayana Maddu +2
We present data structures and algorithms for native implementations of discrete convolution operators over Adaptive Particle Representations (APR) of images on parallel computer a…
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman +2
Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretiz…
Learning physically consistent mathematical models from data using group sparsity
Suryanarayana Maddu, Bevan L. Cheeseman, Christian L. Müller +1
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetr…
Stability selection enables robust learning of partial differential equations from limited noisy data
Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini +1
We present a statistical learning framework for robust identification of partial differential equations from noisy spatiotemporal data. Extending previous sparse regression approac…
Multivariate Newton Interpolation
Michael Hecht, Karl B. Hoffmann, Bevan L. Cheeseman +1
For , and a given function , the polynomial interpolation problem (PIP) is to determine a unisolvent node…
A Quadratic-Time Algorithm for General Multivariate Polynomial Interpolation
M. Hecht, B. L. Cheeseman, K. B. Hoffmann +1
For , and a given function the polynomial interpolation problem (PIP) is to determine a \emph{generic nod…