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Mathematics & Statistics
New mathematics and statistics is posted on arXiv, often long before it appears in a journal. This page shows the newest papers ReadSearch has read, from algebra and geometry to analysis, probability and applied mathematics.
The newest 1,000 papers, from 13 October 2026 to 5 November 2026: 971 arXiv preprints and 29 OpenAlex records.
Newest papers, page 29
A Graph-informed Kriging model for sample-efficient Bayesian optimization of wind farm layouts
Wind farm layout optimization becomes increasingly difficult as turbine count grows, especially when high-fidelity aerodynamic models are used.Causal DAG-Guided Reinforcement Learning for Long-Term Physical Activity Maintenance: Using Prior Data to Inform Future Deployment
Digital health interventions can provide scalable, personalized support for physical activity (PA), yet sustaining PA over the long-term remains difficult.AlgebrasTQD: Computing Algebras and Interfaces in Twisted Quantum Doubles
We present AlgebrasTQD, an open-source code written in GAP (Groups, Algorithms, and Programming), for computing condensable algebras and the corresponding topological boundaries and interfaces of…Analytic Bootstrap Solution for Quantum Mechanics on a Circle
We solve the Schrödinger equation for a particle in the circle with a quantum mechanical bootstrap method.An algebraic characterisation of $n$-qubit pure-state invariants
We study the set of $n$-qubit pure quantum states.Learning on-the-fly quasi-stationarity diagnostics from low-dimensional surrogates
Accelerated dynamics methods, such as parallel replica dynamics, require estimates of the decorrelation time, after which a process trapped in one of its metastable states is distributed according…Minimax-Optimal Small-Loss Bounds for Online Linear Optimisation on $\ell_p$ Balls
We study full-information online linear optimisation on the unit $\ell_p$ ball in $\mathbb{R}^d$, for fixed $2\le p<\infty$ and losses of dual norm at most one.Approximation Families and the Statistical Efficiency of Denoising Score Matching
We study score matching over finite-dimensional approximation spaces generated by gradients of eigenfunctions of the infinitesimal generator of a forward diffusion.Directional Statistics on Circles and Spheres: A New Angle on Data
Two compass bearings near north can have an ordinary arithmetic mean pointing south: the difficulty is that angles live on a circle, not a straight line.Dynamical Tensor Train Approximation for Wigner--Fokker--Planck Equation with Applications to Open Quantum Systems
We develop a dynamical tensor-train method for the Wigner--Fokker--Planck equation arising in continuous-variable open quantum systems.Quantum hydrodynamics from Nambu's unpublished fluid notes
We develop a quantum hydrodynamic theory based on Nambu's unpublished note \emph{A New Look at Fluid Dynamics}.AHEAD: Adaptive Neural Acceleration for Large-Scale Linear Programming
Large-scale linear programs with millions of variables and constraints strain time and memory budgets.Balanced Active Subspaces for Optimal Control via Trajectory Optimization
The curse of dimensionality associated with solving the Hamilton-Jacobi-Bellman equation for the value function remains a key obstacle for real-time optimal feedback control of high-dimensional…Mutation-acyclicity in rank four is decidable but not semialgebraic
We prove that mutation-acyclicity of quivers on four vertices is decidable.A Nonconvex Framework for Joint Sparse Sample Weighting and Parameter Estimation
We propose a joint sparse sample weighting and parameter estimation (SWAPE) framework that jointly optimizes model parameters and sample weights to train a model while simultaneously assigning an…Resolving the Mobility-Equity-Emissions Trilemma: A Causal Machine Learning Framework for Dynamic Tolling Policy
We propose a causal machine learning framework to integrate dynamic tolling for mobility, equity, and climate objectives, resolving a long-standing policy trilemma.The Symmetry of Differential Neural Tangent Kernel under the Orthogonal Group
The differential neural tangent kernel (DNTK) is a tool for analyzing the training dynamics of overparameterized physics informed neural networks.Minimax Regret for Sobolev Kernel-Based Reinforcement Learning
We study the minimax regret rate for episodic kernel-based reinforcement learning, where the expected reward and transition density belong to a Sobolev reproducing kernel Hilbert space, with…Synthetic Data Augmentation for Functional Data: Wasserstein Risk Bounds and Diffusion-Based Conditional Generation
Functional data are costly to collect; samples of tens to hundreds of curves are common.Estimation of covariance matrix for Stokes-like measurement under phase-space deformation
We investigate the signatures of phase-space noncommutativity in an anisotropic oscillator and propose an optomechanical protocol to experimentally characterize it through Stokes-like measurements…
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