Applied Math · Mathematics & Statistics
Numerical Analysis
The newest papers in Numerical Analysis that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
285 papers, from 31 August 2026 to 5 November 2026: 212 arXiv preprints and 73 OpenAlex records.
Get the few papers worth your time in Numerical Analysis every morning.
Follow Numerical Analysis and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 8
Kolmogorov-Arnold Networks for Free-Boundary Partial Differential Equations
We study free-boundary problems within a physics-informed framework using Kolmogorov-Arnold network (KAN) approximations.Flow Matching for Fast Posterior Sampling in Bayesian Inverse Problems
Sampling from the posterior is the central task of computational Bayesian inverse problems.FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport
Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all $n\times m$ point pairs in every Sinkhorn iteration.Learning Closure of Dynamical Systems with Kernel Ridge Regression
We develop a closure modeling framework for identifying missing components of dynamical systems using Kernel Ridge Regression (KRR).Approximation and computation of the geodesic Sinkhorn distance
In [H.The isoperimetric problem for four-dimensional parallelohedra
The honeycomb and Kelvin problems ask how space can be partitioned into cells of equal volume with the least surface area.High-accuracy simulation of Picard HMC, part I: Gaussian cloud correction
We study the problem of sampling from a continuous density $π\propto \exp(-V)$ on $\mathbb R^d$, where $V\in C^2(\mathbb R^d)$ has a $β$-Lipschitz gradient and $π$ satisfies a logarithmic Sobolev…Minimax rates for learning spectral Barron functions by deep ReLU neural networks
We study how well deep neural networks approximate and learn spectral Barron functions.Quantum Frozen--Oseen homotopy analysis method with LCHS for solving nonlinear partial differential equations
Nonlinear partial differential equations (PDEs) underpin computational fluid dynamics, yet resolving nonlinear transport on fine grids remains computationally demanding.LampAttention: Look-Ahead Mixed-Precision FlashAttention for Dedicated Accelerators
While most attention logits can be computed in low precision without degrading numerical stability, current attention kernels fail to exploit this phenomenon.Multifidelity Formulations for Triangular Transport
We develop multifidelity methods for constructing triangular transport maps from samples, when high-fidelity data are scarce but lower-fidelity data are more abundant.Approximation Property of Dropout Neural Networks: Sobolev Rates and Confidence Bounds
The universal approximation property of dropout neural networks does not by itself describe the network size required for an accurate random realization.The Work of Hong Wang
Hong Wang has made several tremendous contributions to harmonic analysis and geometric measure theory.CI-PINN: Causal Integral Physics-Informed Neural Network for Solving Evolution Equations
Physics-informed neural networks (PINNs) solve partial differential equations (PDEs) by incorporating governing physical laws into the training loss.Into the danger zone: stable extrapolation in high-dimensional function and operator learning
Out-of-distribution (OOD) generalization is a central challenge in scientific machine learning.Hybrid Joint-Selective Optimization: Reduced-Space Levenberg-Marquardt Refinement of Low-Dimensional Parameters of Interest
This paper introduces a hybrid joint-selective optimization (HJSO) framework for large-scale numerical problems in which a small subset of trainable quantities is of primary interest.The double descent and Runge phenomena in overparametrized polynomial interpolation
The Runge phenomenon in polynomial interpolation is often considered a classical analogue of the double descent phenomenon in machine learning.Neural topology optimization of ship structures under propulsion machinery vibrations
Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological designs near resonance.Solving Linear Systems in $\widetilde{O}(mn \log \fracκε)$ Bit Operations
We give a deterministic algorithm that solves a nonsingular linear system $Ax=b$, where $A\in\mathbb{R}^{n\times n}$ has $m$ nonzero entries and condition number $κ$, to any relative residual…Broken Symmetry in BF16 Attention: Why FlashAttention Gradients Blow Up Late in Training
BF16 is now standard in large-scale pretraining, including in fused attention kernels such as FlashAttention, and these kernels are widely trusted.