Overview of Efficient Numerical Computing Methods Based on Deep Learning
Year: 2025
Type: article
Abstract: This 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.
Author: Kejun Yang
Institution: China Jiliang University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409715227