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Computations of incompressible flows with velocity boundary conditions require solution of a Poisson equation for pressure with all Neumann boundary conditions. Discretization of such a Poisson equation results in a rank-deficient matrix of…

Numerical Analysis · Mathematics 2022-02-08 Shantanu Shahane , Surya Pratap Vanka

The increasing demand for larger and higher fidelity simulations has made Adaptive Mesh Refinement (AMR) and unstructured mesh techniques essential to focus compute effort and memory cost on just the areas of interest in the simulation…

Graphics · Computer Science 2025-01-23 Xuan Huang , Will Usher , Valerio Pascucci

Computational analysis with the finite element method requires geometrically accurate meshes. It is well known that high-order meshes can accurately capture curved surfaces with fewer degrees of freedom in comparison to low-order meshes.…

Mathematical Software · Computer Science 2024-01-30 Ketan Mittal , Veselin A. Dobrev , Patrick Knupp , Tzanio Kolev , Franck Ledoux , Claire Roche , Vladimir Z. Tomov

Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some…

Machine Learning · Computer Science 2025-11-03 Matin Ansaripour , Shayan Talaei , Giorgi Nadiradze , Dan Alistarh

Distributed algorithms for solving additive or consensus optimization problems commonly rely on first-order or proximal splitting methods. These algorithms generally come with restrictive assumptions and at best enjoy a linear convergence…

Optimization and Control · Mathematics 2017-05-11 Sina Khoshfetrat Pakazad , Christian A. Naesseth , Fredrik Lindsten , Anders Hansson

Local meshless methods obtain higher convergence rates when RBF approximations are augmented with monomials up to a given order. If the order of the approximation method is spatially variable, the numerical solution is said to be p-refined.…

Numerical Analysis · Mathematics 2022-01-28 Mitja Jančič , Jure Slak , Gregor Kosec

Meshless methods are used to solve partial differential equations by approximating differential operators at a node as a weighted sum of values at its neighbours. One of the algorithms for generating nodes suitable for meshless numerical…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-05-11 Jon Vehovar , Miha Rot , Matjaž Depolli , Gregor Kosec

Since the advent of mesh-free methods as a tool for the numerical analysis of systems of Partial Differential Equations (PDEs), many variants of differential operator approximation have been proposed. In this work, we propose a local…

Numerical Analysis · Mathematics 2022-03-07 Mitja Jančič , Gregor Kosec

We present a novel approach for high-order accurate numerical differentiation on unstructured meshes of quadrilateral elements. To differentiate a given function, an auxiliary function with greater smoothness properties is defined which…

Numerical Analysis · Mathematics 2022-05-11 Yulong Pan , Per-Olof Persson

Mesh-free numerical methods offer flexibility in discretising complex geometries, showing potential where mesh-based methods struggle. While high-order approximations can be obtained via consistency correction using linear systems, they…

Computational Physics · Physics 2025-04-18 Lucas Gerken Starepravo , Georgios Fourtakas , Steven Lind , Ajay Harish , Jack R. C. King

We present four high performance hybrid sorting methods developed for various parallel platforms: shared memory multiprocessors, distributed multiprocessors, and clusters taking advantage of existence of both shared and distributed memory.…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-03-04 Thoria Alghamdi , Gita Alaghband

Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for large scattered (unordered) datasets in d-dimensional space. This approach is useful for a higher…

Numerical Analysis · Computer Science 2018-06-21 Zuzana Majdisova , Vaclav Skala

In this work, we consider alternative discretizations for PDEs which use expansions involving integral operators to approximate spatial derivatives. These constructions use explicit information within the integral terms, but treat boundary…

Computational Physics · Physics 2024-11-12 Andrew J. Christlieb , Pierson T. Guthrey , William A. Sands , Mathialakan Thavappiragasm

Feature selection is a process of choosing a subset of relevant features so that the quality of prediction models can be improved. An extensive body of work exists on information-theoretic feature selection, based on maximizing Mutual…

Machine Learning · Computer Science 2016-12-05 Jilin Wu , Soumyajit Gupta , Chandrajit Bajaj

Meshless methods are commonly used to determine numerical solutions to partial differential equations (PDEs) for problems involving free surfaces and/or complex geometries, approximating spatial derivatives at collocation points via local…

Numerical Analysis · Mathematics 2025-10-24 H. Broadley , J. R. C. King , S. J. Lind

In this paper, we present a novel parallel dimension-independent node positioning algorithm that is capable of generating nodes with variable density, suitable for meshless numerical analysis. A very efficient sequential algorithm based on…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-02-04 Matjaž Depolli , Jure Slak , Gregor Kosec

This paper presents a novel p-adaptive, high-order mesh-free framework for the accurate and efficient simulation of fluid flows in complex geometries. High-order differential operators are constructed locally for arbitrary node…

Numerical Analysis · Mathematics 2025-11-27 Ruofeng Feng , Jack R. C. King , Steven J. Lind

A new concept for the higher-order accurate approximation of partial differential equations on manifolds is proposed where a surface mesh composed by higher-order elements is automatically generated based on level-set data. Thereby, it…

Numerical Analysis · Computer Science 2017-10-11 T. P. Fries , D. Schöllhammer

We consider large-scale linear inverse problems in Bayesian settings. Our general approach follows a recent line of work that applies the approximate message passing (AMP) framework in multi-processor (MP) computational systems by storing…

Information Theory · Computer Science 2016-11-17 Junan Zhu , Ahmad Beirami , Dror Baron

Local meshless methods using RBFs augmented with monomials have become increasingly popular, due to the fact that they can be used to solve PDEs on scattered node sets in a dimension-independent way, with the ability to easily control the…

Numerical Analysis · Mathematics 2022-01-28 Mitja Jančič , Jure Slak , Gregor Kosec
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