Mathematical optimization, Engineering design process, Algorithm, Surrogate model and Kriging are his primary areas of study. Many of his research projects under Mathematical optimization are closely connected to Gaussian process with Gaussian process, tying the diverse disciplines of science together. Andy J. Keane has included themes like Scheme and Function, Systems engineering in his Engineering design process study.
His Algorithm study combines topics in areas such as High fidelity, Optimal design, Robustness and Maxima and minima. His study looks at the relationship between Surrogate model and topics such as Benchmark, which overlap with Adaptive learning, Adaptive system and Nonlinear programming. His research in Kriging intersects with topics in Design of experiments, Sample, Noise and Extension.
His primary scientific interests are in Mathematical optimization, Engineering design process, Finite element method, Kriging and Genetic algorithm. His research integrates issues of Algorithm and Optimal design in his study of Mathematical optimization. His study looks at the relationship between Engineering design process and fields such as Systems engineering, as well as how they intersect with chemical problems.
His Finite element method research is multidisciplinary, relying on both Vibration, Numerical analysis, Mathematical analysis and Mechanical engineering. The study of Kriging is intertwined with the study of Computational fluid dynamics in a number of ways. His Genetic algorithm study incorporates themes from Design of experiments and Artificial intelligence.
His main research concerns Mathematical optimization, Kriging, Mechanical engineering, Combustor and Engineering design process. Mathematical optimization is closely attributed to Function in his work. His biological study spans a wide range of topics, including Minimum weight, Polygon mesh, Optimal design and Topology.
His research in Mechanical engineering tackles topics such as Finite element method which are related to areas like Surrogate model, Gas compressor, Set and Aileron. His Combustor research is multidisciplinary, relying on both Computational fluid dynamics and Combustion chamber. The concepts of his Engineering design process study are interwoven with issues in Process flowsheeting, Design process, Iterative design and Engineering management.
Andy J. Keane spends much of his time researching Mathematical optimization, Mechanical engineering, Surrogate model, Engineering design process and Kriging. His research in Mathematical optimization is mostly concerned with Robust design optimization. His work carried out in the field of Mechanical engineering brings together such families of science as Face and Finite element method.
His Surrogate model study combines topics from a wide range of disciplines, such as Analytic function, Selection and Limit. His studies in Engineering design process integrate themes in fields like Operations research, Iterative and incremental development, Search engine and Aerospace. Andy J. Keane has researched Kriging in several fields, including Minimum weight, Fuselage, Optimal design and Multi-objective optimization.
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Engineering Design via Surrogate Modelling: A Practical Guide
Alexander I. J Forrester;András Sóbester;A. J. Keane.
Recent advances in surrogate-based optimization
Alexander I.J. Forrester;Andy J. Keane.
Progress in Aerospace Sciences (2009)
Engineering Design via Surrogate Modelling
Alexander I. J. Forrester;Andrs Sbester;Andy J. Keane.
Multi-fidelity optimization via surrogate modelling
Alexander I.J Forrester;András Sóbester;Andy J Keane.
Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences (2007)
Meta-Lamarckian learning in memetic algorithms
Yew Soon Ong;A.J. Keane.
IEEE Transactions on Evolutionary Computation (2004)
Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling
Yew S. Ong;Prasanth B. Nair;Andrew J. Keane.
AIAA Journal (2003)
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
Zongzhao Zhou;Yew Soon Ong;P.B. Nair;A.J. Keane.
systems man and cybernetics (2007)
Computational Approaches for Aerospace Design: The Pursuit of Excellence
Andy J. Keane;Prasanth B. Nair.
Statistical Improvement Criteria for Use in Multiobjective Design Optimization
Andy J. Keane.
AIAA Journal (2006)
On the Design of Optimization Strategies Based on Global Response Surface Approximation Models
András Sóbester;Stephen J. Leary;Andy J. Keane.
Journal of Global Optimization (2005)
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