Artificial Intelligence in Manufacturing: Concepts and Methods
Editat de Masoud Soroush, Richard D Braatzen Limba Engleză Paperback – 25 ian 2024
The book addresses educational challenges needed for widespread implementation of AI and also provides detailed technical instructions for the implementation of AI methods. Drawing on research in computer science, physics and a range of engineering disciplines, this book tackles the interdisciplinary challenges of the subject to introduce new thinking to important manufacturing problems.
- Presents AI concepts from the computer science field using language and examples designed to inspire engineering graduates
- Provides worked examples throughout to help readers fully engage with the methods described
- Includes concepts that are supported by definitions for key terms and chapter summaries
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (2) | 926.57 lei 5-7 săpt. | |
| ELSEVIER SCIENCE – 25 ian 2024 | 926.57 lei 5-7 săpt. | |
| ELSEVIER SCIENCE – 25 ian 2024 | 926.65 lei 5-7 săpt. |
Preț: 926.65 lei
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Specificații
ISBN-13: 9780323991346
ISBN-10: 0323991343
Pagini: 372
Dimensiuni: 152 x 229 mm
Greutate: 0.54 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0323991343
Pagini: 372
Dimensiuni: 152 x 229 mm
Greutate: 0.54 kg
Editura: ELSEVIER SCIENCE
Public țintă
Researchers in industry and academia with an interest in advanced manufacturing or industrial applications of AI.Cuprins
1. Data‐driven Physics‐based Digital Twins
2. Hybrid Modeling Approach Integrating PLS Models with First-principles Knowledge
3. Dynamical Systems-Guided Learning of PDEs from Data
4. Learning First-principles Knowledge from Data
5. Actual Learning through Machine Learning
6. Iterative Cross Learning
7. Learning an Algebraic Model from Data
8. Data‐driven Optimization Algorithms
9. Interpretable Machine Learning
10. Learning Science and Algorithms
11. Reinforcement Learning
12. Machine Learning: Trends, Perspectives, and Prospects
13. Artificial Intelligence: Trends, Perspectives, and Prospects
14. Artificial Intelligence Education for Chemical Engineers
2. Hybrid Modeling Approach Integrating PLS Models with First-principles Knowledge
3. Dynamical Systems-Guided Learning of PDEs from Data
4. Learning First-principles Knowledge from Data
5. Actual Learning through Machine Learning
6. Iterative Cross Learning
7. Learning an Algebraic Model from Data
8. Data‐driven Optimization Algorithms
9. Interpretable Machine Learning
10. Learning Science and Algorithms
11. Reinforcement Learning
12. Machine Learning: Trends, Perspectives, and Prospects
13. Artificial Intelligence: Trends, Perspectives, and Prospects
14. Artificial Intelligence Education for Chemical Engineers