All Research Papers Published In Journal of Computational Science

Research Article
10.1016/j.jocs.2026.102854

A finite difference moving mesh method for a class of singularly perturbed parabolic problems

Jul 01, 2026
Journal of Computational Science
Zhanyang Yan + 1 more +1

Research Article
10.1016/j.jocs.2026.102850

Development and evaluation of a black-box optimization framework for weather-intervention design

Jun 01, 2026
Journal of Computational Science
Yuta Higuchi + 5 more +5

Research Article
10.1016/j.jocs.2026.102829

GPU-oriented numerical algorithm to estimate formation factor of porous materials

May 01, 2026
Journal of Computational Science
Vadim Lisitsa + 4 more +4

Research Article
10.1016/j.jocs.2026.102814

Iterative quantum-assisted least squares optimization with convergence guarantees

Apr 01, 2026
Journal of Computational Science
Supreeth Mysore Venkatesh + 4 more +4

Research Article
10.1016/j.jocs.2026.102807

Discovery of Bus Loop Scheduling strategies with reinforcement learning to minimize commuters’ waiting and travel times

Apr 01, 2026
Journal of Computational Science
Andri Pradana + 1 more +1

In this paper, we investigate the application of two reinforcement learning methods, known as the Dueling Double Deep Q-Network and Soft Actor-Critic to discover bus scheduling strategies and compare them against conventional approaches. In particular, we look into real-time control strategies where buses may choose to stay or leave at bus stops. We explore both waiting time and travel time as the optimization objectives. The results for uniform bus frequency show that average waiting time can be reduced by allowing buses to stay longer at stops with higher passengers’ arrival rate but at the cost of increased average travel time. This is also supported by our analytical calculation on a theoretical bus loop model. We then apply our method to a model based on a real world bus loop in Nanyang Technological University. The results highlight the potential benefit of reinforcement learning methods to find novel strategies that can be better than conventional approaches. The similar performance of the two distinct reinforcement learning methods also serves as independent verification of the validity of the strategies obtained. This is an extended version of our ICCS 2025 conference paper “Bus Loop Scheduling with Dueling Double Deep Q Network” Pradana and Chew (2025), with the main addition of the application of the Soft Actor-Critic method which has to be modified to handle the optimization problem described in this paper.

Research Article
10.1016/j.jocs.2026.102820

ECA-RRT*: A robotic arm path planning algorithm based on environment complexity adaptive heuristic strategy

Apr 01, 2026
Journal of Computational Science
Huiyuan Zhu + 3 more +3

Research Article
1
10.1016/j.jocs.2026.102804

Classification of piano performers with deep learning models

Apr 01, 2026
Journal of Computational Science
Jan Mycka + 1 more +1

Research Article
10.1016/j.jocs.2026.102817

Reliable physics-informed neural networks for Navier–Stokes simulations. Can we trust AI-generated numerical simulations?

Apr 01, 2026
Journal of Computational Science
Tomasz Służalec + 5 more +5

Research Article
10.1016/j.jocs.2026.102805

GRASFormer: A computational framework for gradient-regularized and entropy-stabilized multi-task transformer optimization

Apr 01, 2026
Journal of Computational Science
Pulkit Dwivedi + 1 more +1

Research Article
10.1016/j.jocs.2026.102806

New way to control hidden memory chaotic attractors of fractional order systems beyond equilibrium points

Apr 01, 2026
Journal of Computational Science
Bichitra Kumar Lenka + 1 more +1