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Showing 10 of 10 academic articles

An Optimal Fourth-Order Finite Difference Scheme for the Helmholtz Equation Based on the Technique of Matched Interface Boundary

Dongsheng Cheng, Jianjun Chen, Guangqing Long • Discrete Dynamics in Nature and Society (2021)

In this paper, a new optimal fourth-order 21-point finite difference scheme is proposed to solve the 2D Helmholtz equation numerically, with the technique of matched interface boundary (MIB) utilized to treat boundary problems. For the appr...

DOAJ Open Access (DOAJ) DOI: 10.1155/2021/2539272

Numerical Simulation of an Intramedullary Elastic Nail: Expansion Phase and Load-Bearing Behavior

Giulia Pascoletti, Filippo Cianetti, Giovanni Putame, Mara Terzini et al. • Frontiers in Bioengineering and Biotechnology (2018)

The Marchetti-Vicenzi's nail is an intramedullary device where six curved nails are kept straight by a closing ring in order to allow their insertion into the medullary canal of a long bone; in a following step, these nails stabilize the fr...

DOAJ Open Access (DOAJ) DOI: 10.3389/fbioe.2018.00174

Exploring the Relative Importance and Interactive Impacts of Explanatory Variables of the Built Environment on Ride-Hailing Ridership by Using the Optimal Parameter-Based Geographical Detector (OPGD) Model

Zhenbao Wang, Shuyue Liu, Yuchen Zhang, Xin Gong et al. • Applied Sciences (2023)

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an...

DOAJ Open Access (DOAJ) DOI: 10.3390/app13042180

Deep learning in computational mechanics: a review

Leon Herrmann, Stefan Kollmannsberger • Computational Mechanics (2024)

Abstract The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies w...

OPENALEX cc-by DOI: https://doi.org/10.1007/s00466-023-02434-4

Non-linear and probabilistic state discretization functions for enhanced discrete reinforcement learning: Application on wind turbine pitch control

Alberto Gil-Maciá, J. Enrique Sierra-García, Matilde Santos • Machine Learning with Applications (2026)

Discrete reinforcement learning (RL) offers interpretability advantages compared to continuous reinforcement learning. Neural networks used in continuous RL behave like black boxes that do not show an obvious relationship between the system...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mlwa.2026.100919