IMA Journal of Numerical Analysis

Papers
(The H4-Index of IMA Journal of Numerical Analysis is 16. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)
ArticleCitations
Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs114
Optimal-order error estimates of finite element approximations to variable-order time-fractional diffusion equations without regularity assumptions of the true solutions63
Blow-up of error estimates in time-fractional initial-boundary value problems61
An adaptive BDF2 implicit time-stepping method for the phase field crystal model39
Accurately computing the log-sum-exp and softmax functions33
Anderson acceleration for contractive and noncontractive operators25
Estimates on the generalization error of physics-informed neural networks for approximating PDEs24
Conforming and nonconforming VEMs for the fourth-order reaction–subdiffusion equation: a unified framework23
Automatic rational approximation and linearization of nonlinear eigenvalue problems21
The Euler–Maruyama scheme for SDEs with irregular drift: convergence rates via reduction to a quadrature problem18
Energy-preserving methods for nonlinear Schrödinger equations18
A numerical-analysis-focused comparison of several finite volume schemes for a unipolar degenerate drift-diffusion model18
Conforming and nonconforming virtual element methods for a Kirchhoff plate contact problem17
Simulation of McKean–Vlasov SDEs with super-linear growth17
Adaptive cubic regularization methods with dynamic inexact Hessian information and applications to finite-sum minimization17
Domain decomposition preconditioners for high-order discretizations of the heterogeneous Helmholtz equation16
A Banach space mixed formulation for the unsteady Brinkman–Forchheimer equations16
Error estimates for physics-informed neural networks approximating the Navier–Stokes equations16
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