scholarly journals Bayesian Experimental Design: efficient exploration of the design space

2019 ◽  
Author(s):  
Lida Mavrogonatou ◽  
Vladislav Vyshemirsky
Keyword(s):  
Experimental Design ◽  
Design Space ◽  
Efficient Exploration

scholarly journals Bayesian optimization with adaptive surrogate models for automated experimental design

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Bowen Lei ◽  
Tanner Quinn Kirk ◽  
Anirban Bhattacharya ◽  
Debdeep Pati ◽  
Xiaoning Qian ◽  
...  
Keyword(s):  
Experimental Design ◽  
Design Space ◽  
Materials Science ◽  
Scientific Discovery ◽  
Surrogate Models ◽  
Multivariate Adaptive Regression Splines ◽  
Bayesian Optimization ◽  
Objective Functions ◽  
Additive Regression ◽  
Efficient Exploration

AbstractBayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies. This can have a significant impact on modern scientific discovery, in particular autonomous materials discovery, which can be viewed as an optimization problem aimed at looking for the maximum (or minimum) point for the desired materials properties. The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions. In this paper, we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure, namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees. They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions. Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.

Ramjet Powered Missile Design Using a Genetic Algorithm

2007 ◽  
Vol 7 (2) ◽  
pp. 167-173 ◽  
Author(s):  
Roy J. Hartfield ◽  
Rhonald M. Jenkins ◽  
John E. Burkhalter
Keyword(s):  
Genetic Algorithm ◽  
Performance Prediction ◽  
Design Space ◽  
System Optimization ◽  
Design Strategy ◽  
Optimization Process ◽  
Preliminary Design ◽  
Efficient Exploration ◽  
Typical Design ◽  
Design Scenario

A methodology for developing optimized designs for symmetric-centerbody ramjet powered missiles, using a genetic algorithm as the driver for the system optimization process, has been developed. The methodology described in this paper allows for a comprehensive but efficient exploration of the design space. This global optimization process is made possible by performance prediction codes, which can provide preliminary design-level accuracy very efficiently. This work demonstrates the first truly comprehensive design strategy for this type of device. The paper contains a discussion of the methodology and shows results for a typical design scenario.

scholarly journals Dynamic Planning of Experiments for the Optimisation of Managerial Scheduling

2016 ◽  
Vol 64 (6) ◽  
pp. 2047-2051
Author(s):  
Tomáš Macák ◽  
Jan Hron
Keyword(s):  
Experimental Design ◽  
Time Management ◽  
Design Space ◽  
Dynamic Scheduling ◽  
Local Optima ◽  
Personal Lives ◽  
Steepest Ascent ◽  
Factors Influencing ◽  
Adjustment Factors ◽  
Planning Of Experiments

Time management has a crucial role in organizations and also in our personal lives. Managerial scheduling is an important tool for the time management, especially It can serve as a tool for the first phase, of time management - namely for effective planning. This paper focusses on finding the best possible setting for determining significant the best layout for activities according to the criteria of urgency and importance using a modified steepest ascent method, which can be referred as dynamic scheduling. This term indicates the nature of the method; wherein the experimental design space is changed to look for the best conditions for adjustment factors influencing a managerial process. Existing methods for layout optimization mentioned in the literature and conventionally implemented in practice have only shown local optima.

scholarly journals Two-Stage OTA Sizing Optimization Using Bio-Inspired Algorithms

2019 ◽  
Vol 14 (3) ◽  
pp. 1-10
Author(s):  
Anderson Fortes ◽  
Luiz Antonio Da Silva Jr ◽  
Robson Domanski ◽  
Alessandro Girardi
Keyword(s):  
Integrated Circuit ◽  
Design Space ◽  
Cuckoo Search ◽  
Two Stage ◽  
Silicon Area ◽  
Circuit Sizing ◽  
Transconductance Amplifier ◽  
Analog Part ◽  
Optimization Heuristics ◽  
Efficient Exploration

The analog part of a mixed-signal integrated circuit represents a great amount of the circuit sizing effort. It is necessary to size each device separately and, in cases with several variables, the design space becomes quite large. The analog integrated circuit sizing can be modeled as an optimization problem and solved by optimization heuristics. In this work, we compare three bio-inspired heuristics to size a two-stage CMOS Miller operational transconductance amplifier: Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Firefly Algorithm (FA). The goal is to evaluate the applicability of these heuristics for the analog sizing problem and to determine the best configuration of the algorithms parameters for optimizing performance of the generated circuit, mainly power consumption and silicon area. Results show that PSO and CS are more suitable to find optimized solutions, while FA presents less efficient exploration of the design space. Although PSO is faster and generates good solutions, the best overall solution was achieved with CS algorithm.

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