All Research Papers Published In Mathematics and Computers in Simulation

Research Article
10.1016/j.matcom.2025.10.015

An age-gender-structured mathematical model to study the optimization of COVID-19 vaccination programs.

Mar 01, 2026
Mathematics and computers in simulation
Gilberto González-Parra + 4 more +4

Research Article
10.1016/j.matcom.2026.02.003

Symmetric stabilized FEM for time-fractional convection–diffusion–reaction equations

Feb 19, 2026
Mathematics and Computers in Simulation
Naveed Ahmed + 2 more +2

Research Article
10.1016/s0378-4754(25)00445-8

News of IMACS

Feb 01, 2026
Mathematics and Computers in Simulation

Research Article
10.1016/s0378-4754(25)00442-2

Editorial Board

Feb 01, 2026
Mathematics and Computers in Simulation

Research Article
10.1016/j.matcom.2025.07.003

Tamed Euler interpolation multilevel Monte Carlo method for quadratic forward backward stochastic differential equations

Feb 01, 2026
Mathematics and Computers in Simulation
Qiang Han + 1 more +1

Research Article
10.1016/j.matcom.2025.07.041

A modified contraction-type method for solving monotone inclusion problem in Hilbert spaces

Feb 01, 2026
Mathematics and Computers in Simulation
Linan Wang + 3 more +3

Research Article
10.1016/j.matcom.2025.07.061

Competitive exclusion and coexistence of a nonlocal diffusive two-strain SIS epidemic model with Neumann boundary condition

Feb 01, 2026
Mathematics and Computers in Simulation
Junyuan Yang + 1 more +1

Research Article
10.1016/j.matcom.2025.07.053

On the use of the principle of maximum entropy to improve the robustness of bivariate spline least-squares approximation

Feb 01, 2026
Mathematics and Computers in Simulation
P Amodio + 2 more +2

We consider fitting a bivariate spline regression model to data using a weighted least-squares cost function, with weights that sum to one to form a discrete probability distribution. By applying the principle of maximum entropy, the weight distribution is determined by maximizing the associated entropy function. This approach, previously applied successfully to polynomials and spline curves, enhances the robustness of the regression model by automatically detecting and down-weighting anomalous data during the fitting process. To demonstrate the effectiveness of the method, we present applications to two image processing problems and further illustrate its potential through two synthetic examples. Unlike the standard ordinary least-squares method, the maximum entropy formulation leads to a nonlinear algebraic system whose solvability requires careful theoretical analysis. We provide preliminary results in this direction and discuss the computational implications of solving the associated constrained optimization problem, which calls for dedicated iterative algorithms. These aspects suggest natural directions for further research on both the theoretical and algorithmic fronts.

Research Article
10.1016/j.matcom.2025.07.044

Acceleration of convergence in approximate solutions of Urysohn integral equations with Green’s kernels

Feb 01, 2026
Mathematics and Computers in Simulation
Shashank K Shukla + 1 more +1

Research Article
10.1016/j.matcom.2025.05.023

On accurate closed-form Coiflet solutions of nonlinear Korteweg–de Vries–Burgers equation for shallow water wave by a time-integration wavelet homotopy method

Jan 01, 2026
Mathematics and Computers in Simulation
Qiang Yu + 2 more +2