Numerical analysis & scientific computing
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- Numerical Construction of Quasi-Periodic Solutions for Nonlinear PDEs: I. Bounded Perturbation
- An overview of machine learning-enhanced iterative methods for systems of linear and nonlinear equations
- A Column-Wise Reflection Method with Safeguarded Anderson Acceleration for the Continuous Sylvester Matrix Equation
- A higher order family of iterative methods for approximating inverse and generalized inverse matrices
- Fixed-Grid Reversibility of High-Order Splittings for Nonlinear Schrödinger Equations with Arbitrary-Axis 3D Rotation
- Well-posed by Design: Learning Constitutive Laws from Velocity Data using Convex Neural Network Potentials
- On the Growth Factor of Random Matrices
- Singular parameters and missing limits in neural PDE solvers
- Quantitative Γ-Convergence: A Variational Extrapolation Framework
- Transparent Boundary Conditions for the Time-Fractional Heat Equation on Metric Graphs
- Surface Stokes-Cahn-Hilliard System: Analysis and Structure-Preserving Discretization
- One Extra Dimension Suffices for the Complete-Graph Squared-Stress
- Delta Redistribution: a variation of State Redistribution for steady-state embedded boundary computations
- A Mixed-Precision Model for Matrix-Splitting Methods
- IGA-KAN: Isogeometric Analysis with Physics-Informed Closed-Form Kolmogorov-Arnold Networks for Forward and Inverse PDEs
- Sharp dimensional analysis of midpoint methods for Langevin sampling
- Nonparametric face-based finite element methods on complex hybrid three-dimensional meshes
- Polynomial Acceleration of Alternating Projections: Chebyshev Acceleration, Convergence Rates, and Retractions
- Near-Exact Computation of Independent Chip Model Placement Probabilities for Thousands of Players
- Quasi-Trefftz spaces for the first-order time-harmonic Maxwell's equations
- On solid angles and simplex shape measures
- On the Sequentially Semiseparable Structure of SDE-Induced Kernels and Efficient Algorithms for Kernel-Based Estimation Problems
- Global error estimators for parametric monotone nonlinearities and neural approximations
- A Class of Function-Preserving Neural Network Operators with Applications to Modulated Signal Denoising
- Inferring physical fields in coupled systems with unknown parameters from incomplete observations using physics-constrained attentive neural operators