math.NA

Papers filed under math.NA on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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1,081 to 1,140 of 1,816

WHFast: A fast and unbiased implementation of a symplectic Wisdom-Holman integrator for long term gravitational simulations
Hanno Rein, Daniel Tamayo
astro-ph.EPastro-ph.IMmath.NAarXiv:1506.01084v12015
Multilevel Training for Kolmogorov Arnold Networks
Ben S. Southworth, Jonas A. Actor, Graham Harper +1
cs.LGcs.AImath.NAarXiv:2603.04827v12026
Off-Grid Point-Scatterer Localization from Sparse Limited-Aperture Data via Hankel Completion
Jingzhi Li, Xiliang Lu, Juntao You
math.NAcs.NAarXiv:2609.08665v12026
Failure-informed adaptive sampling for PINNs
Zhiwei Gao, Liang Yan, Tao Zhou
math.NAstat.MLarXiv:2210.00279v32022
A discontinuous finite element method for the hydroelastic analysis of submerged structures
Zachary J. Wegert, Oriol Colomés, Michael H. Meylan +2
math.NAcs.NAarXiv:2609.08313v22026
Error analysis of the L1 method on graded and uniform meshes for a fractional-derivative problem in two and three dimensions
Natalia Kopteva
math.NAarXiv:1709.09136v62017
Phase-field modeling of fluid-driven dynamic cracking in porous media
Shuwei Zhou, Xiaoying Zhuang, Timon Rabczuk
math.NAcs.CEarXiv:2309.02438v12023
Data Driven Governing Equations Approximation Using Deep Neural Networks
Tong Qin, Kailiang Wu, Dongbin Xiu
math.NAcs.LGcs.NEarXiv:1811.05537v12018
Multi-scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains
Bo Wang, Wenzhong Zhang, Wei Cai
math.NAcs.NAphysics.comp-pharXiv:2009.12729v22020
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Sifan Wang, Hanwen Wang, Paris Perdikaris
cs.LGmath.NAstat.MLarXiv:2103.10974v12021
Primal dual methods for Wasserstein gradient flows
Jose A. Carrillo, Katy Craig, Li Wang +1
math.NAcs.NAarXiv:1901.08081v22019
Unconditional convergence and optimal error estimates of a Galerkin-mixed FEM for incompressible miscible flow in porous media
Buyang Li, Weiwei Sun
math.NAarXiv:1207.1762v32012
Operator inference for non-intrusive model reduction with quadratic manifolds
Rudy Geelen, Stephen Wright, Karen Willcox
math.NAcs.NAarXiv:2205.02304v42022
A variational physics-informed graph neural network for heterogeneous solid mechanics
Aashay Rajan Yadav, Amiya Prakash Das, Ratna Kumar Annabattula
math.NAcs.LGarXiv:2609.10983v12026
Gradient Sparsification for Communication-Efficient Distributed Optimization
Jianqiao Wangni, Jialei Wang, Ji Liu +1
cs.LGmath.NAstat.MLarXiv:1710.09854v12017
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Salvatore Cuomo, Vincenzo Schiano di Cola, Fabio Giampaolo +3
cs.LGcs.AImath.NAarXiv:2201.05624v42022
Adaptive Gradient Descent without Descent
Yura Malitsky, Konstantin Mishchenko
math.OCcs.LGmath.NAarXiv:1910.09529v22019
When and why PINNs fail to train: A neural tangent kernel perspective
Sifan Wang, Xinling Yu, Paris Perdikaris
cs.LGmath.NAstat.MLarXiv:2007.14527v12020
Analysis of Floating-Point Matrix Multiplication Computed via Integer Arithmetic
Ahmad Abdelfattah, Jack Dongarra, Massimiliano Fasi +2
math.NAcs.MScs.NAarXiv:2506.11277v32025
Unified Gas-kinetic Wave-Particle methods I: Continuum and Rarefied Gas Flow
Chang Liu, Yajun Zhu, Kun Xu
math.NAcs.NAarXiv:1811.07141v12018
Fourier neural operator for real-time simulation of 3D dynamic urban microclimate
Wenhui Peng, Shaoxiang Qin, Senwen Yang +3
cs.LGcs.CEcs.NAarXiv:2308.03985v22023
CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method
Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi +2
cs.LGcs.CEmath.NAarXiv:2110.15832v22021
On the limiting behavior of parameter-dependent network centrality measures
Michele Benzi, Christine Klymko
math.NAcs.SIphysics.soc-pharXiv:1312.6722v72013
Optimally weighted loss functions for solving PDEs with Neural Networks
Remco van der Meer, Cornelis Oosterlee, Anastasia Borovykh
math.NAarXiv:2002.06269v42020
A Solution Accurate, Efficient and Stable Unsplit Staggered Mesh Scheme for Three Dimensional Magnetohydrodynamics
Dongwook Lee
physics.comp-phastro-ph.HEcs.NAarXiv:1303.6988v12013
A note on the Joint Spectral Radius
Antonio Cicone
math.NAarXiv:1502.01506v12015
Numerical Approximation of Incompressible Navier-Stokes Equations Based on an Auxiliary Energy Variable
Lianlei Lin, Suchuan Dong
physics.flu-dynmath.NAphysics.comp-pharXiv:1804.10859v12018
Deep learning of free boundary and Stefan problems
Sifan Wang, Paris Perdikaris
math.NAcs.LGcs.NAarXiv:2006.05311v12020
Automating the Finite Element Method
Anders Logg
math.NAcs.NAarXiv:1112.0433v12011
Implementing FFTs in Practice
Steven G. Johnson, Matteo Frigo
math.NAarXiv:2602.23525v12026
Quantum linear system solver based on time-optimal adiabatic quantum computing and quantum approximate optimization algorithm
Dong An, Lin Lin
quant-phmath.NAarXiv:1909.05500v32019
Compact Finite Difference Approximations for Space Fractional Diffusion Equations
Han Zhou, WenYi Tian, Weihua Deng
math.NAarXiv:1204.4870v12012
Paved with Good Intentions: Analysis of a Randomized Block Kaczmarz Method
Deanna Needell, Joel A. Tropp
math.NAarXiv:1208.3805v32012
Optimal asymptotic bounds for spherical designs
Andriy Bondarenko, Danylo Radchenko, Maryna Viazovska
math.MGmath.COmath.NAarXiv:1009.4407v32010
The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
Zhouchen Lin, Minming Chen, Yi Ma
math.OCeess.SYmath.NAarXiv:1009.5055v32010
Trapezoidal methods for fractional differential equations: theoretical and computational aspects
Roberto Garrappa
math.NAarXiv:1912.09878v12019
An assessment of phase field fracture: crack initiation and growth
P. K. Kristensen, C. F. Niordson, E. Martínez-Pañeda
cs.CEmath.NAphysics.app-pharXiv:2103.05443v22021
Convergence Analysis and Error Estimates for a Second Order Accurate Finite Element Method for the Cahn-Hilliard-Navier-Stokes System
Amanda E. Diegel, Cheng Wang, Xiaoming Wang +1
math.NAarXiv:1606.02668v12016
Stiff Neural Ordinary Differential Equations
Suyong Kim, Weiqi Ji, Sili Deng +2
math.NAcs.LGarXiv:2103.15341v32021
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach
Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis +1
stat.MLcs.ITcs.LGarXiv:1609.03240v22016
A second order in time, uniquely solvable, unconditionally stable numerical scheme for Cahn-Hilliard-Navier-Stokes equation
Daozhi Han, Xiaoming Wang
math.NAarXiv:1407.7048v12014
Phase field fracture modelling using quasi-Newton methods and a new adaptive step scheme
Philip K. Kristensen, Emilio Martínez-Pañeda
math.NAcond-mat.mtrl-sciarXiv:1912.08620v12019
The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?
Stefano Markidis
math.NAcs.DCphysics.comp-pharXiv:2103.09655v22021
Localization of Elliptic Multiscale Problems
Axel Malqvist, Daniel Peterseim
math.NAarXiv:1110.0692v42011
The Galerkin Finite Element Method for A Multi-term Time-Fractional Diffusion equation
Bangti Jin, Raytcho Lazarov, Yikan Liu +1
math.NAarXiv:1401.8049v12014
Composing Scalable Nonlinear Algebraic Solvers
Peter R. Brune, Matthew G. Knepley, Barry F. Smith +1
math.NAcs.MSarXiv:1607.04254v12016
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem
Matthew J. Colbrook, Vegard Antun, Anders C. Hansen
cs.LGcs.CVcs.NEarXiv:2101.08286v22021
LSMR: An iterative algorithm for sparse least-squares problems
David Fong, Michael Saunders
cs.MSmath.NAarXiv:1006.0758v22010
Randomized Numerical Linear Algebra: Foundations & Algorithms
Per-Gunnar Martinsson, Joel Tropp
math.NAarXiv:2002.01387v32020
PyHessian: Neural Networks Through the Lens of the Hessian
Zhewei Yao, Amir Gholami, Kurt Keutzer +1
cs.LGmath.NAarXiv:1912.07145v32019
On conservation laws of Navier-Stokes Galerkin discretizations
Sergey Charnyi, Timo Heister, Maxim A. Olshanskii +1
math.NAarXiv:1605.09763v22016
MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems
Sou-Cheng T. Choi, Christopher C. Paige, Michael A. Saunders
math.NAcs.CEcs.NAarXiv:1003.4042v32010
In-Context Operator Learning with Data Prompts for Differential Equation Problems
Liu Yang, Siting Liu, Tingwei Meng +1
cs.LGcs.NAmath.NAarXiv:2304.07993v32023
Boosting of Image Denoising Algorithms
Yaniv Romano, Michael Elad
cs.CVmath.NAarXiv:1502.06220v22015
The exponential scalar auxiliary variable (E-SAV) approach for phase field models and its explicit computing
Zhengguang Liu, Xiaoli Li
math.NAmath.AParXiv:1912.09263v22019
Numerical analysis of nonlinear eigenvalue problems
Eric Cancès, Rachida Chakir, Yvon Maday
math.NAarXiv:0905.1645v22009
A splitting approach for the fully nonlinear and weakly dispersive Green-Naghdi model
Philippe Bonneton, Florent Chazel, David Lannes +2
math.NAphysics.ao-pharXiv:1005.1586v12010
A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks
Suchuan Dong, Naxian Ni
physics.comp-phcs.LGmath.NAarXiv:2007.07442v12020
A new class of efficient and robust energy stable schemes for gradient flows
Jie Shen, Jie Xu, Jiang Yang
math.NAarXiv:1710.01331v12017
Super-Resolution from Noisy Data
Emmanuel Candes, Carlos Fernandez-Granda
cs.ITmath.NAarXiv:1211.0290v32012

math.NA

WHFast: A fast and unbiased implementation of a symplectic Wisdom-Holman integrator for long term gravitational simulations

Multilevel Training for Kolmogorov Arnold Networks

Off-Grid Point-Scatterer Localization from Sparse Limited-Aperture Data via Hankel Completion

Failure-informed adaptive sampling for PINNs

A discontinuous finite element method for the hydroelastic analysis of submerged structures

Error analysis of the L1 method on graded and uniform meshes for a fractional-derivative problem in two and three dimensions

Phase-field modeling of fluid-driven dynamic cracking in porous media

Data Driven Governing Equations Approximation Using Deep Neural Networks

Multi-scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains

Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets

Primal dual methods for Wasserstein gradient flows

Unconditional convergence and optimal error estimates of a Galerkin-mixed FEM for incompressible miscible flow in porous media

Operator inference for non-intrusive model reduction with quadratic manifolds

A variational physics-informed graph neural network for heterogeneous solid mechanics

Gradient Sparsification for Communication-Efficient Distributed Optimization

Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Adaptive Gradient Descent without Descent

When and why PINNs fail to train: A neural tangent kernel perspective

Analysis of Floating-Point Matrix Multiplication Computed via Integer Arithmetic

Unified Gas-kinetic Wave-Particle methods I: Continuum and Rarefied Gas Flow

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate

CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method

On the limiting behavior of parameter-dependent network centrality measures

Optimally weighted loss functions for solving PDEs with Neural Networks

A Solution Accurate, Efficient and Stable Unsplit Staggered Mesh Scheme for Three Dimensional Magnetohydrodynamics

A note on the Joint Spectral Radius

Numerical Approximation of Incompressible Navier-Stokes Equations Based on an Auxiliary Energy Variable

Deep learning of free boundary and Stefan problems

Automating the Finite Element Method

Implementing FFTs in Practice

Quantum linear system solver based on time-optimal adiabatic quantum computing and quantum approximate optimization algorithm

Compact Finite Difference Approximations for Space Fractional Diffusion Equations

Paved with Good Intentions: Analysis of a Randomized Block Kaczmarz Method

Optimal asymptotic bounds for spherical designs

The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices

Trapezoidal methods for fractional differential equations: theoretical and computational aspects

An assessment of phase field fracture: crack initiation and growth

Convergence Analysis and Error Estimates for a Second Order Accurate Finite Element Method for the Cahn-Hilliard-Navier-Stokes System

Stiff Neural Ordinary Differential Equations

Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach

A second order in time, uniquely solvable, unconditionally stable numerical scheme for Cahn-Hilliard-Navier-Stokes equation

Phase field fracture modelling using quasi-Newton methods and a new adaptive step scheme

The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?

Localization of Elliptic Multiscale Problems

The Galerkin Finite Element Method for A Multi-term Time-Fractional Diffusion equation

Composing Scalable Nonlinear Algebraic Solvers

Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem

LSMR: An iterative algorithm for sparse least-squares problems

Randomized Numerical Linear Algebra: Foundations & Algorithms

PyHessian: Neural Networks Through the Lens of the Hessian

On conservation laws of Navier-Stokes Galerkin discretizations

MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems

In-Context Operator Learning with Data Prompts for Differential Equation Problems

Boosting of Image Denoising Algorithms

The exponential scalar auxiliary variable (E-SAV) approach for phase field models and its explicit computing

Numerical analysis of nonlinear eigenvalue problems

A splitting approach for the fully nonlinear and weakly dispersive Green-Naghdi model

A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks

A new class of efficient and robust energy stable schemes for gradient flows

Super-Resolution from Noisy Data