Applied mathematics and machine learning
- Author
- Qun Li and Aihua Wood
- Publication Year
- 2024
- Publisher
- MDPI
- Language
- English
- Document Type
- Book
- Faculty / Subject Heading
- Computer Science
- Download Book Read book
The simultaneous availability of large datasets and high-performance computing capability in recent years has enabled the rapid development of powerful machine learning algorithms. On the one hand, state-of-the-art machine learning techniques have transformed many areas of science and engineering; on the other hand, theoretical discoveries in mathematical algorithms, differential equations, and statistical inferences, to name a few, have provided the foundation for the exploration of new multidisciplinary models for solving practical problems. This Special Issue endeavors to continue the journey that started in our previous Special Issue (Applied Mathematics and Computational Physics) by providing a platform for researchers from both academia and industry, as well as government, to present their new computational methods that have engineering and physics applications.
Keywords: Machine learning /Applied mathematics / Compressive strength / Data envelopment analysis / Dynamics / Efficiency / Electron microscopes / Entropy / Industry 4.0 / Mathematics / Multiple criteria decision making / Operational risk
