98,897 research outputs found
Replication or exploration? Sequential design for stochastic simulation experiments
We investigate the merits of replication, and provide methods for optimal
design (including replicates), with the goal of obtaining globally accurate
emulation of noisy computer simulation experiments. We first show that
replication can be beneficial from both design and computational perspectives,
in the context of Gaussian process surrogate modeling. We then develop a
lookahead based sequential design scheme that can determine if a new run should
be at an existing input location (i.e., replicate) or at a new one (explore).
When paired with a newly developed heteroskedastic Gaussian process model, our
dynamic design scheme facilitates learning of signal and noise relationships
which can vary throughout the input space. We show that it does so efficiently,
on both computational and statistical grounds. In addition to illustrative
synthetic examples, we demonstrate performance on two challenging real-data
simulation experiments, from inventory management and epidemiology.Comment: 34 pages, 9 figure
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Sensitivity analysis of expensive black-box systems using metamodeling
Simulations are becoming ever more common as a tool for designing complex
products. Sensitivity analysis techniques can be applied to these simulations
to gain insight, or to reduce the complexity of the problem at hand. However,
these simulators are often expensive to evaluate and sensitivity analysis
typically requires a large amount of evaluations. Metamodeling has been
successfully applied in the past to reduce the amount of required evaluations
for design tasks such as optimization and design space exploration. In this
paper, we propose a novel sensitivity analysis algorithm for variance and
derivative based indices using sequential sampling and metamodeling. Several
stopping criteria are proposed and investigated to keep the total number of
evaluations minimal. The results show that both variance and derivative based
techniques can be accurately computed with a minimal amount of evaluations
using fast metamodels and FLOLA-Voronoi or density sequential sampling
algorithms.Comment: proceedings of winter simulation conference 201
Fuzzy local linear approximation-based sequential design
When approximating complex high-fidelity black box simulators with surrogate models, the experimental design is often created sequentially. LOLA-Voronoi, a powerful state of the art method for sequential design combines an Exploitation and Exploration algorithm and adapts the sampling distribution to provide extra samples in non-linear regions. The LOLA algorithm estimates gradients to identify interesting regions, but has a bad complexity which results in long computation time when simulators are high-dimensional. In this paper, a new gradient estimation approach for the LOLA algorithm is proposed based on Fuzzy Logic. Experiments show the new method is a lot faster and results in experimental designs of comparable quality
Integrated structure/control law design by multilevel optimization
A new approach to integrated structure/control law design based on multilevel optimization is presented. This new approach is applicable to aircraft and spacecraft and allows for the independent design of the structure and control law. Integration of the designs is achieved through use of an upper level coordination problem formulation within the multilevel optimization framework. The method requires the use of structure and control law design sensitivity information. A general multilevel structure/control law design problem formulation is given, and the use of Linear Quadratic Gaussian (LQG) control law design and design sensitivity methods within the formulation is illustrated. Results of three simple integrated structure/control law design examples are presented. These results show the capability of structure and control law design tradeoffs to improve controlled system performance within the multilevel approach
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