activity
20242026
most citedKernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning

6 citations · 6 across the 4 of their papers we have counts for

collaborators
Showing math.NAShow all

5 papers · 1 filter

math.NA2026

Efficient and Robust Carathéodory-Steinitz Pruning of Positive Discrete Measures

Filip Bělík, Jesse Chan, Akil Narayan

In many applications, one seeks to approximate integration against a positive measure of interest by a positive discrete measure: a numerical quadrature rule with positive weights.…

math.NA2026

Structure-Preserving Discontinuous Galerkin Methods for Stochastic Shallow Water Equations

Yekaterina Epshteyn, Akil Narayan, Yinqian Yu

Shallow water equations (SWE) are fundamental models in fluid dynamics that are essential for studying a wide range of geophysical and engineering phenomena. In many practical appl…

math.NA2025

Entropy stable reduced order modeling of nonlinear conservation laws using discontinuous Galerkin methods

Ray Qu, Akil Narayan, Jesse Chan

Reduced order models (ROMs) are inexpensive surrogate models that reduce costs associated with many-query scenarios. Current methods for constructing entropy stable ROMs for nonlin…

math.NA2025

An Optimal Weighted Least-Squares Method for Operator Learning

John Turnage, Matthew Lowery, John Jakeman +3

We consider the problem of learning an unknown, possibly nonlinear operator between separable Hilbert spaces from supervised data. Inputs are drawn from a prescribed probability me…

math.NA2024

Energy Stable and Structure-Preserving Algorithms for the Stochastic Galerkin System of 2D Shallow Water Equations

Yekaterina Epshteyn, Akil Narayan, Yinqian Yu

Shallow water equations (SWE) are fundamental nonlinear hyperbolic PDE-based models in fluid dynamics that are essential for studying a wide range of geophysical and engineering ph…