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 13
Selective boundary condition reduction via learned error gating
Parametric PDEs can admit different boundary conditions with different accuracy and computational cost.CAST: Canonical Approximate Schur Tree for Approximate Cholesky on Graphs
Graph-data workloads such as diffusion estimation, ranking, semi-supervised learning, and network optimization often solve many Laplacian or symmetric diagonally dominant M-matrix (SDDM) systems…Support Discovery With Iteratively Reweighted Least Squares for Fixed-Charge Network Flow
The fixed-charge network flow problem (FCNFP) couples continuous flow allocation with discrete arc-activation decisions, making it a canonical but computationally challenging model for a variety of…A Block Tensor Train Burer-Monteiro Framework for Low-Rank Quantum State Tomography
Quantum state tomography is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evaluating the performance of quantum devices.A Discontinuous Galerkin Method for the Intrinsic Beam Model with Kelvin-Voigt Damping
We present an energy-stable discontinuous Galerkin (DG) spatial discretization of the intrinsic beam equations with Kelvin-Voigt damping.Radial Projectively Induced Canonical Kähler Metrics: Rigidity and Classification
We study radial Kähler metrics on domains of $\mathbb{C}^n$, $n\geq 2$, admitting a Kähler immersion into a finite- or infinite-dimensional complex projective space.Smoothed Picard Hamiltonian Monte Carlo
We develop a new low-accuracy sampler, called \emph{smoothed Picard Hamiltonian Monte Carlo}, which combines Gaussian smoothing, Picard iteration, and higher-order discretization.Constitutive State-Space Modeling of Path-Dependent Plasticity: A Resolution-Consistent and Parallelizable Computational Framework
Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state evolution limits parallel training and whose…A Systematic Analysis of Automatic Differentiation versus Discretization-based Constraints for Physics-Informed PDE Solvers
Physics-informed neural networks (PINNs) represent a growing frontier in using artificial intelligence to solve partial differential equations (PDEs).Poisson Image Denoising Using Minimax Concave and Reweighted $\ell_1$ Penalties: Nonblind and Blind Approaches
Images are important tools in various sciences.Introductory Notes on Learning$^2$
Although machine learning can be used to predict the evolution of physical systems from data, a formulation that learns only the system state at each time leaves the temporal and dynamical structure…Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments
Smart agriculture platforms usually bundle AI diagnosis, IoT sensing and decision push into a single package, so the green benefit attributable to each component remains unclear and…Optimal Spectral Lower Bounds and Nonradial Nonlinear Asymptotic Stability of a Family of Three-Dimensional Keller--Segel Self-Similar Blow-Up Solutions
This paper studies the spectral properties and nonlinear asymptotic stability of a family of finite-time self-similar blow-up solutions to the three-dimensional Keller--Segel system constructed by…Nonlinear elliptic homogenization with the parametric Deep Ritz method
Elliptic homogenization is used to determine coarse-grained properties of materials with features on small scales.A Gamma envelope and sharp moment inequalities for Gaussian quadratic forms
We study extremal absolute central moments of Gaussian quadratic forms under a fixed Frobenius norm.PRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting
Cryptocurrency markets exhibit extreme volatility and non-stationary dynamics that challenge conventional forecasting methods.Shallow neural network approximation in mixed Sobolev spaces
We investigate the best $L_2$ approximation of mixed Sobolev spaces by shallow neural networks with $n$ neurons and general activation functions.A Nuclear-Norm Lower Bound for Dithered Scalar Quantization of Matrix Products
We consider the problem of minimizing error in quantized matrix multiplication $C=AB$.Construction of Finite Hilbert--Pólya Matrices from Weil's Explicit Formula
Starting from the Riemann--$Ξ$ specialization of Weil's explicit formula, we construct finite real-symmetric Prime--Weil matrices $(\mathbf S)$ from pole, archimedean, and finite prime-power data.Bounded ratios for Lorentzian polynomials
We study multiplicative inequalities among the coefficients of Lorentzian polynomials through the notion of bounded ratios.