Surrogate modeling: tricks that endured the test of time and some recent developments
Tasks such as analysis, design optimization, and uncertainty quantification can be
computationally expensive. Surrogate modeling is often the tool of choice for reducing the …
computationally expensive. Surrogate modeling is often the tool of choice for reducing the …
Bayesian optimization with active learning of design constraints using an entropy-based approach
The design of alloys for use in gas turbine engine blades is a complex task that involves
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys
Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and
exploit materials design spaces. To date, most BO approaches to materials design have …
exploit materials design spaces. To date, most BO approaches to materials design have …
Adaptive active subspace-based efficient multifidelity materials design
D Khatamsaz, A Molkeri, R Couperthwaite, J James… - Materials & Design, 2021 - Elsevier
Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …
property (PSP) relationships to produce materials with targeted properties. Recently, we …
Multi-fidelity Bayesian Optimization in Engineering Design
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization
(BO), MF BO has found a niche in solving expensive engineering design optimization …
(BO), MF BO has found a niche in solving expensive engineering design optimization …
A framework of adaptive fuzzy control and optimization for nonlinear systems with output constraints
D Bao, X Liang, SS Ge, Z Hao, B Hou - Information Sciences, 2022 - Elsevier
This paper presents a framework for adaptive fuzzy control and optimization of nonlinear
systems subject to uncertainties and disturbances. The barrier Lyapunov function (BLF) …
systems subject to uncertainties and disturbances. The barrier Lyapunov function (BLF) …
Correlation-concerned Bayesian optimization for multi-objective airfoil design
Z Liu, X Qu, X Liu, H Lyu - Aerospace Science and Technology, 2022 - Elsevier
Airfoil design based on Bayesian optimization generally involves high-fidelity simulations,
whose crux in terms of efficiency has always challenged existing optimization frameworks …
whose crux in terms of efficiency has always challenged existing optimization frameworks …
A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties
Optimization for different tasks like material characterization, synthesis, and functional
properties for desired applications over multi-dimensional control parameter and function …
properties for desired applications over multi-dimensional control parameter and function …
Asynchronous Multi-Information Source Bayesian Optimization
D Khatamsaz, R Arroyave… - Journal of …, 2024 - asmedigitalcollection.asme.org
Resource management in engineering design seeks to optimally allocate while maximizing
the performance metrics of the final design. Bayesian optimization (BO) is an efficient design …
the performance metrics of the final design. Bayesian optimization (BO) is an efficient design …
Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization
A Kesireddy, FA Medrano - Algorithms, 2024 - mdpi.com
Optimization is a process of minimizing or maximizing a given objective function under
specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are …
specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are …