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 11
Gaussian-process surrogate indicators for residual-based adaptive GMsFEM
Residual-based adaptive GMsFEM for high-contrast elliptic problems repeatedly evaluates local weighted $H^{-1}$ indicators on every coarse neighborhood, making indicator evaluation a recurring cost…The Critical Line Algorithm and the Constrained LASSO: One Curve, Two Literatures
Many statistical procedures compute an entire solution path rather than a single estimator.MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting
Tracking neurite morphology over time helps characterize structural changes during neuronal development and deterioration, but long-term time-lapse imaging is resource-intensive and difficult to…Cost-Accuracy Trade-offs: Neural Operator vs Classical Numerical Solver
Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying coefficients, initial conditions, forcing terms…Variational objectives for amortized Bayesian inference in inverse problems: The role of posterior conditioning
Variational autoencoders (VAEs) offer an efficient approach to amortized Bayesian inference for inverse problems, but posterior accuracy can depend strongly on the choice of variational…Higher-Order Approximation of Exit Functionals in Sampling-Based Stochastic Model Predictive Control
Safety evaluation in sampling-based stochastic model predictive control often requires numerical estimation of exit functionals.FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation
Data assimilation aims to infer the state of complex dynamical systems based on observational data.Beyond PINNs: A Unified Gauss--Newton and Petrov--Galerkin Framework for Neural and Hybrid PDE Solvers
Physics-informed neural networks and finite element methods provide two different paradigms for the numerical approximation of partial differential equations: the former are commonly trained by…Stiefel-AdamW: Geometry-Aware AdamW for Linear Factorization Blocks
A pervasive structural pattern in modern deep learning is the linear factorization block: a submodule of the form $W = BA$ in which two parameter matrices are multiplied directly, with no…Domain-decomposed Evolutional Deep Neural Network with Random Features for Transient Pressure Diffusion with Discontinuous and High-Contrast Coefficients
Transient pressure diffusion in heterogeneous porous media becomes difficult to resolve efficiently when permeability is discontinuous and spans several orders of magnitude.Finding New Limit Points of Mahler Measure by Methods of Missing Data Restoration
It is well known that the set of Mahler measures of single variable polynomial has limit points of which a list established by D. Boyd and M. Mossinghoff has been extended through approaches based…Regularity of asymptotically axisymmetric solutions to the 3D Navier-Stokes equations with analytic forcing
OpenAI~\cite{OpenAIManuscript} has recently announced a proof of finite time singularity formation for the 3D Navier-Stokes equations, in the presence of a $C^\infty$-smooth body force.2D reaction-diffusion model-based biopsy simulation for dynamic tumor growth parameter estimation
Once diagnosed, cancer requires a fast, reliable and preferably cost-efficient assessment of the current state and potential progression of the disease.HiLNO: A Hierarchical Latent Neural Operator with Multi-Scale Supervision for PDEs on General Geometries
Latent neural operators improve the efficiency of operator learning for partial differential equations (PDEs) by performing the main computation on compact latent representations.Stiefel Attention: When the Geometry of Transformer Projection Matrices Dominates Optimizer Choice---and When It Does Not
The query and key projections $\WQ,\WK$ in attention are almost always trained by Euclidean optimizers with no constraint on their geometry.The Pólya--Szegő conjecture for convex polygons with many sides
For every sufficiently large N, we prove that the regular N-gon uniquely minimizes the first Dirichlet eigenvalue among convex polygons of prescribed area with at most N sides, up to rigid motions.A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure
Approximating the input-output behavior of a multivariable black-box function from limited data is challenging when blind to the importance of its inputs and their interactions.Near-Optimal Nonconvex Matrix Completion
We study nonconvex methods for matrix completion, the problem of recovering a low-rank matrix from a subset of its entries.High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics
The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the…Approximating Measures on Function Spaces: Transport and Truncation
Measures on function spaces arise throughout Bayesian inverse problems and generative modeling, often with low-dimensional structure relative to a tractable reference measure.