227 papers
This paper establishes the Freidlin--Wentzell large deviations principle for the stochastic Cahn--Hilliard equation with small noise and proves the convergence of the one-point large deviations rate function for its spatial finite difference method by utilizing -convergence of objective functions and overcoming non-Lipschitz drift challenges through discrete interpolation inequalities.
This paper establishes the central limit theorem for the temporal average of the backward Euler–Maruyama method applied to stochastic ordinary differential equations with super-linearly growing drift coefficients, deriving the result through direct analysis for sub-optimal deviation orders and via a Poisson equation approach for the optimal strong order.
This paper establishes the strong convergence rate of an Euler-Maruyama numerical scheme for one-dimensional stochastic differential equations with distributional drifts in Hölder-Zygmund spaces, supported by theoretical bounds and numerical implementation.
This paper presents a complete solution to the infinite quantum signal processing problem for arbitrary Szegő functions by introducing the Riemann-Hilbert-Weiss algorithm, which provably computes individual phase factors independently and stably using polylogarithmic precision and polynomial computational cost.
This paper proposes a dynamic domain semi-Lagrangian method for stochastic Vlasov equations driven by transport noises that significantly reduces computational costs through volume-preserving characteristics and domain adaptation, while providing a first-order convergence analysis that partially resolves a recent conjecture on numerical convergence.
This paper proposes a method to enable effective stratified sampling in high-dimensional spaces by using neural active manifolds to identify a one-dimensional latent space that captures model variability, allowing for the creation of input partitions that align with model level sets to significantly reduce variance in uncertainty propagation.
This paper presents an error analysis and a new regularization parameter selection strategy for the iterated Golub-Kahan-Tikhonov method applied to discretized ill-posed operator equations, demonstrating its superior accuracy over both standard Golub-Kahan-Tikhonov and iterated Arnoldi-Tikhonov methods, particularly for non-symmetric matrices.
This paper establishes the quasi-optimality of the Crouzeix-Raviart finite element method for nonlinear -Laplace-type problems by proving that its error is bounded by the best-approximation error and a data oscillation term, while also deriving a novel localized a priori error estimate for the conforming lowest-order Lagrange FEM.
This paper introduces a novel computational framework that utilizes Legendre time-dimensional reduction to transform the inverse initial-data problem for compressible anisotropic Navier-Stokes equations into a solvable system of elliptic equations, enabling the robust and accurate reconstruction of initial velocity fields from noisy boundary observations.
This paper establishes the rigorous convergence of hyperbolic approximations to various higher-order PDEs, including the KdV and Kuramoto-Sivashinsky equations, by proving that weak solutions of the approximations converge to smooth solutions of the limiting problems, a result supported by numerical evidence.
This paper presents a quadrature-based model reduction framework for Lagrangian hydrodynamics that utilizes a strongly energy-conservative variant of the empirical quadrature procedure to achieve near machine-precision total energy conservation while maintaining accuracy comparable to standard methods.
This paper demonstrates that the union of randomly digitally shifted Korobov polynomial lattice point sets achieves a star discrepancy with an inverse that depends linearly on the dimension, thereby narrowing the search for explicit constructions from a continuum to a finite set of candidates.
This paper proposes an auto-adaptive sampling method for Physics Informed Neural Networks (PINNs) that utilizes arbitrary problem-specific heuristics to accurately resolve interfacial regions in Allen-Cahn equations without post-hoc resampling, demonstrating superior performance over residual-adaptive frameworks.
This paper presents numerically stable, closed-form algorithms for computing the eigenvalues of real, diagonalizable $3 \times 3$ matrices using four specific invariants, demonstrating that these methods are both more accurate for repeated eigenvalues and approximately ten times faster than the LAPACK library while maintaining comparable precision.
This paper establishes a minimax theory for operator learning in Hilbert spaces, proving that even with higher regularity assumptions, learning generic Lipschitz operators from noisy samples inevitably suffers from a curse of sample complexity where the risk cannot decay at any algebraic rate.
This paper introduces a novel numerical method that combines a functional equation for the Laplace-Stieltjes transform with a moment-matching technique to efficiently and stably compute the density of the Kesten-Stigum limit for supercritical Galton-Watson processes with arbitrary offspring laws.
This paper introduces a novel Geometric Structure-Preserving Interpolation (-SPIN) method that utilizes geodesic elements and a projection-based relaxation of rotation-deformation coupling to achieve stable, locking-free finite-strain Cosserat micropolar elasticity simulations, particularly in the asymptotic couple-stress limit.
This paper introduces and validates two new structure-preserving discontinuous Galerkin methods, SWIPD-L and SIPGD-L, for the Cahn-Hilliard-Navier-Stokes equations with degenerate mobility, demonstrating that they achieve optimal convergence, preserve key physical properties like mass conservation and energy dissipation, and offer significant computational savings on adaptive meshes compared to existing approaches.
This paper introduces a non-separable progressive multivariate WENO scheme tailored for cell-average data that achieves high-order accuracy and non-oscillatory stability in image processing, demonstrating superior performance over linear Lagrange reconstruction through theoretical analysis and numerical experiments.
This paper proposes a post-processing method that significantly improves the accuracy of physics-informed neural networks (PINNs) by finding the best approximation in a function space associated with the network, achieving errors four to five orders of magnitude lower than standard PINNs while enabling transfer learning and providing a metric for optimal basis function selection.