P
Prasanth B. Nair
Researcher at University of Toronto
Publications - 126
Citations - 4262
Prasanth B. Nair is an academic researcher from University of Toronto. The author has contributed to research in topics: Basis (linear algebra) & Finite element method. The author has an hindex of 30, co-authored 122 publications receiving 3822 citations. Previous affiliations of Prasanth B. Nair include University of Southampton.
Papers
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Journal ArticleDOI
Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling
TL;DR: The essential backbone of the framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning that leverages surrogate models for solving computationally expensive design problems with general constraints on a limited computational budget.
Journal ArticleDOI
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
TL;DR: A novel surrogate-assisted evolutionary optimization framework that uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search.
Book
Computational Approaches for Aerospace Design: The Pursuit of Excellence
Andy J. Keane,Prasanth B. Nair +1 more
TL;DR: This text explores how computer-aided analysis has revolutionized aerospace engineering, providing a comprehensive coverage of the latest technologies underpinning advanced computational design.
Journal ArticleDOI
Max-min surrogate-assisted evolutionary algorithm for robust design
TL;DR: This paper presents a novel evolutionary algorithm based on the combination of a max-min optimization strategy with a Baldwinian trust-region framework employing local surrogate models for reducing the computational cost associated with robust design problems.
Book ChapterDOI
Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems
TL;DR: This chapter presents frameworks that employ surrogate models for solving computationally expensive optimization problems on a limited computational budget and the key factors responsible for the success of these frameworks are discussed.