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20182026
most citedKernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning

6 citations · 17 across the 15 of their papers we have counts for

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9 papers · 1 filter

math.NA2026

Constructive Tchakaloff results and well-conditioned quadrature through randomized least squares

Filip Bělík, Akil Narayan, John Turnage

We consider using randomized least squares to construct quadrature rules exact on a subspace of functions. Using new conditions that we call relative admissibility and reference we…

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

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.NA2025

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.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.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…

math.NA2024

Conformal Finite Element Methods for Nonlinear Rosenau-Burgers-Biharmonic Models

Ankur, Ram Jiwari, Akil Narayan

We present a novel and comparative analysis of finite element discretizations for a nonlinear Rosenau-Burgers model including a biharmonic term. We analyze both continuous and mixe…

math.NA2023

Energy Stable and Structure-Preserving Schemes for the Stochastic Galerkin Shallow Water Equations

Dihan Dai, Yekaterina Epshteyn, Akil Narayan

The shallow water flow model is widely used to describe water flows in rivers, lakes, and coastal areas. Accounting for uncertainty in the corresponding transport-dominated nonline…

math.NA2018★ 3 cited

Certified reduced basis methods for fractional Laplace equations via extension

Harbir Antil, Yanlai Chen, Akil Narayan

Fractional Laplace equations are becoming important tools for mathematical modeling and prediction. Recent years have shown much progress in developing accurate and robust algorith…