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Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing
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Powerful algorithms as key enablers for scientific computing in Reduced-Order Modeling.
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Detalji o proizvodu
| Publisher | Springer |
| Publication date | April 20, 2018 |
| Edition | 2018th |
| Language | English |
| Print length | 188 pages |
| ISBN-10 | 3319753185 |
| ISBN-13 | 978-3319753188 |
| Item Weight | 14.4 ounces (408.24 grams) |
| Dimensions | 6.14 x 0.5 x 9.21 inches (15.6 x 1.3 x 23.4 cm) |
Who Should Buy?
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Researchers in Engineering
This book provides essential ROM techniques for engineers looking to advance their simulation and optimization methodologies.
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Graduate Students
Ideal for students pursuing advanced studies in computational methods, offering clear explanations and practical applications of ROM.
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Data Scientists
Data scientists seeking to enhance computational efficiency through reduced-order modeling techniques will benefit greatly from this resource.
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Beginners in Computing
Individuals new to scientific computing may find the advanced concepts and algorithms challenging without foundational knowledge.
OPIS PROIZVODA
Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing
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Electric Editorial Review
Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing is a comprehensive book published by Springer on April 20, 2018. This 188-page edition is written in English and provides readers with advanced algorithms that are essential for scientific computing. The book, weighing 14.4 ounces and measuring 6.14 x 0.5 x 9.21 inches, delves into methods designed to enhance simulation efficiency and optimize results, making it a vital resource for researchers and practitioners in the field. Its practical insights have been well-received, particularly the clarity in presenting complex concepts.
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Pros
- Advanced algorithms for scientific computing
- Clear presentation of complex concepts
- Useful for both research and practical application
- Well-structured with valuable insights
- Compact size for easy reading
Protiv
- Some concepts may require prior knowledge in the field
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Značajke i prednosti
- Explores advancements in model order reduction for simulation and optimization.
- Offers out-of-the-box algorithmic solutions for scientific computing.
- Covers reduced basis methods for industrial challenges.
- Includes MOR approaches for electrochemical processes.
- Targeted at research experts, practitioners, and graduate students.
- A must-have resource for enhancing efficiency in scientific computing.
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