Probabilistic optimisation of composite structures (Record no. 567653)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02076 a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250922150424.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250917b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781800616844 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.7 |
| Item number | Y8p |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Yoo, Kwangkyu Alex |
| 245 ## - TITLE STATEMENT | |
| Title | Probabilistic optimisation of composite structures |
| Remainder of title | machine learning for design optimisation |
| Statement of responsibility, etc | Kwangkyu Alex Yoo, Omar Bacarreza and M. H. Ferri Aliabadi |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | World Scientific |
| Year of publication | 2025 |
| Place of publication | London |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xi, 192p |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | Computational and experimental methods in structures |
| 490 ## - SERIES STATEMENT | |
| Series statement | / edited by Ferri M. H. Aliabadi |
| Volume number/sequential designation | ; v. 15 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book introduces an innovative approach to multi-fidelity probabilistic optimisation for aircraft composite structures, addressing the challenge of balancing reliability with computational cost. Probabilistic optimisation pursues statistically reliable and robust solutions by accounting for uncertainties in data, such as material properties and geometry tolerances. Traditional approaches using high-fidelity models, though accurate, are computationally expensive and time-consuming, especially when using complex methods such as Monte Carlo simulations and gradient calculations.For the first time, the proposed multi-fidelity method combines high- and low-fidelity models, enabling high-fidelity models to focus on specific areas of the design space, while low-fidelity models explore the entire space. Machine learning technologies, such as artificial neural networks and nonlinear autoregressive Gaussian processes, fill information gaps between different fidelity models, enhancing model accuracy. The multi-fidelity probabilistic optimisation framework is demonstrated through the reliability-based and robust design problems of aircraft composite structures under a thermo-mechanical environment, showing acceptable accuracy and reductions in computational time. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Design optimisation |
| -- | Machine learning |
| -- | Composite structure in aircraft design |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Aliabadi, M. H. Ferri |
| -- | Bacarreza, Omar |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Under Process | General Stacks | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 15/09/2025 | 2 | 5817.24 | 629.7 Y8p | A187036 | 7756.32 | Books |