Metamodel-based multidisciplinary design optimization methods for aerospace system
Renhe Shi,Renhe Shi,Teng Long,Teng Long,Nianhui Ye,Nianhui Ye,Yufei Wu,Yufei Wu,Zhao Wei,Zhao Wei,Liu Zhenyu,Liu Zhenyu +11 more
- 01 Sep 2021
- Vol. 5, Iss: 3, pp 185-215
TL;DR: This paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, meetamodeling techniques, state-of-the-art meetingamodels-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms.
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Abstract: The design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.
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References
•Book
Neural Networks: A Comprehensive Foundation
Simon Haykin
- 16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
TL;DR: In this article, the authors introduce physics-informed neural networks, which are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations.
11.6K
•Book
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Raymond H. Myers,Douglas C. Montgomery +1 more
- 29 Aug 1995
TL;DR: Using a practical approach, this book discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques.
11.2K
Efficient Global Optimization of Expensive Black-Box Functions
TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.
Physics-informed machine learning
George Em Karniadakis,Ioannis G. Kevrekidis,Lu Lu,Paris Perdikaris,Sifan Wang,Liu Yang +5 more
- 01 Jun 2021
TL;DR: Some of the prevailing trends in embedding physics into machine learning are reviewed, some of the current capabilities and limitations are presented and diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems are discussed.
3.7K