253 papers
This paper proposes an unsupervised, surrogate-assisted framework that automatically generates and optimizes free-form planar antenna topologies for IoT applications by combining a classifier for topology selection with a bi-stage gradient-based tuning process, as validated through numerical experiments on bandwidth-enhanced patch antennas.
This paper presents a bi-stage optimization framework that automates the design of pixel-based planar antennas by first globally optimizing pixel interconnections to determine topology and then refining the structure with a surrogate-assisted local search to meet performance specifications.
This paper bridges the gap between theoretical estimates and numerical evidence for the local discontinuous Galerkin method applied to convection-diffusion equations by establishing new approximation results for Gauss-Radau projections, thereby proving optimal convergence rates even for solutions with limited spatial regularity.
This paper proposes an implicit-explicit trust region method that leverages the specific Hessian structure of Landau-type free energy functionals to efficiently compute second-order stationary points, enabling the identification of physically stable phases like the FDDD structure in the Landau-Brazovskii model with provable convergence guarantees.
This paper proposes a novel multivariate, data-dependent partition of unity method based on Moving Least Squares and Weighted Essentially Non-Oscillatory techniques to achieve high-order accuracy in smooth regions while effectively suppressing spurious oscillations near discontinuities.
This paper proposes a novel semi-implicit numerical scheme based on a stochastic scalar auxiliary variable (SSAV) reformulation for the stochastic Cahn--Hilliard equation with multiplicative noise, which incorporates Itô correction terms to achieve optimal strong convergence of order one-half while preserving the energy evolution law.
This paper presents a statistically robust framework for reconstructing metal–semiconductor contact regions by combining topological gradients with shape optimization, establishing a central limit theorem for convergence and utilizing a key parameter to refine interface sensitivity and distinguish true structural features from noise.
This paper presents a comparative numerical study of six graph-based splitting algorithms specialized for linear subspaces, identifying optimal relaxation parameters and analyzing iteration counts to reveal performance patterns that may inform future theoretical analysis.
This paper presents a non-intrusive, multi-fidelity reduced-order modeling framework that utilizes Optimal Transport-based displacement interpolation to efficiently correct low-fidelity models and construct accurate parametric surrogates for complex, nonlinear two-phase flow simulations.
This paper validates the legitimacy and effectiveness of a newly proposed high-frequency fast direct solver for electromagnetic integral equations on non-canonical geometries by leveraging semiclassical microlocal results to analyze its asymptotic spectral properties.
This paper introduces Exp-ParaDiag, a novel time-parallel framework that integrates exponential integrators to solve parabolic PDEs and nonlinear problems with rigorous convergence guarantees and temporal accuracy ranging from first to sixth order.
This paper establishes a strengthened comparison theorem for the extreme eigenvalues of sums of independent random symmetric matrices against Gaussian counterparts, leveraging this result to improve eigenvalue bounds across various fields and providing the first complete proof of the injectivity properties for sparse random dimension reduction maps conjectured by Nelson & Nguyen.
This paper proposes an annealing-based algorithm that solves partial differential equations by transforming discretized linear systems into generalized eigenvalue problems, enabling efficient computation of eigenvectors to arbitrary precision without increasing the number of variables.