JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v9i2.9952ArticleOverview of Efficient Numerical Computing Methods Based on Deep Learninghttps://artdesignp.com/journal/JERA/9/2/10.26689/jera.v9i2.9952YangKejun2025922025-04-02This article reviews the application and progress of deep learning in efficient numerical computing methods. Deep learning, as an important branch of machine learning, provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain. The article explores the application of deep learning in solving partial differential equations, optimizing problems, and data-driven modeling, and analyzes its advantages in computational efficiency, accuracy, and adaptability. At the same time, this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency, interpretability, and generalization ability, and proposes strategies and future development directions for integrating with traditional numerical methods.

Liu D, Chen Q, Wang X, 2024, Deep Learning Method for Solving Linear Integral Equations with Primitive Function Transformation. Computational Physics, 41(5): 651–662.

Li Q, Zhu R, Li S, et al., 2024, Overview of Numerical Calculation Methods for Electrothermal Coupling of Converter Transformers. High Voltage Technology, 50(10): 4734–4748.

Niu Q, Dong S, 2023, Research on Numerical Calculation Method of Infrared Radiation from Rocket Engine Spray Flames (Invited). Optoelectronic Technology Application, 38(1): 1–10.

Hu J, Zhu H, Huang X, et al, 2024, Forest Mobile Robot Tree Trunk Detection based on Deep Learning. Forest Engineering, 40(4): 109–114.

Yin H, Nan Z, 2024, Investor Sentiment Mining based on Deep Learning and its Impact on Stock Price Collapse Risk. Journal of Central University of Finance and Economics, (3): 36–56.

Zhang H, Zhang H, 2022, Research on the Prediction Method of Effective Proliferation Factor of Core Based on Convolutional Neural Network Model. Modern Applied Physics, 13(2): 6.

Yang C, 2021, Review on Theoretical Model and Numerical Methods of Hydraulic Fracturing. Yunnan Chemical Industry, 48(1): 17–20.

Li X, Zhang P, Chen G, et al., 2019, The Rising Behavior of Bubbles and Interfacial Mass Transfer in Liquid: Experimental Study and Numerical Simulation. Advances in Chemical Engineering, 38(2): 740–751.

Dinsmore D, Alexander P, 2012, A Critical Discussion of Deep and Surface Processing: What It Means, How It Is Measured, the Role of Context, and Model Specification. Educational Psychology Review, (24): 499–567.

Chang Z, Martin V, Tammy S, 2009, A Cross-cultural Study of Online Collaborative Learning. Multicultural Education & Technology Journal, 3(1): 33–46.

Gerry G, Desiree J, Paul A. Kirschner, 2016, Changing Learning Behavior: Self-efficacy and Goal Orientation in PBL Groups in Higher Education. International Journal of Educational Research, 75: 146–158.