Creating multivariable response surfaces by the two-stage adaptive sampling techniques and its application in the optimization of grounding grids

2006 ◽  
Vol 42 (4) ◽  
pp. 1131-1134 ◽  
Author(s):  
J. Zou ◽  
J. Guo ◽  
B. Zhang ◽  
J.L. He ◽  
J. Lee ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Response Surfaces ◽  
Sampling Techniques ◽  
Two Stage ◽  
Grounding Grids

On Adaptive Sampling for Single and Multi-Response Bayesian Surrogate Models

2006 ◽  
Author(s):  
David A. Romero ◽  
Cristina H. Amon ◽  
Susan Finger
Keyword(s):  
Experimental Design ◽  
Adaptive Sampling ◽  
Design Space Exploration ◽  
A Priori ◽  
Computer Experiments ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Sampling Techniques ◽  
Design And Optimization ◽  
Sampling Points

In order to reduce the time and resources devoted to design-space exploration during simulation-based design and optimization, the use of surrogate models, or metamodels, has been proposed in the literature. Key to the success of metamodeling efforts are the experimental design techniques used to generate the combinations of input variables at which the computer experiments are conducted. Several adaptive sampling techniques have been proposed to tailor the experimental designs to the specific application at hand, using the already-acquired data to guide further exploration of the input space, instead of using a fixed sampling scheme defined a priori. Though mixed results have been reported, it has been argued that adaptive sampling techniques can be more efficient, yielding better surrogate models with less sampling points. In this paper, we address the problem of adaptive sampling for single and multi-response metamodels, with a focus on Multi-stage Multi-response Bayesian Surrogate Models (MMBSM). We compare distance-optimal latin hypercube sampling, an entropy-based criterion and the maximum cross-validation variance criterion, originally proposed for one-dimensional output spaces and implemented in this paper for multi-dimensional output spaces. Our results indicate that, both for single and multi-response surrogate models, the entropy-based adaptive sampling approach leads to models that are more robust to the initial experimental design and at least as accurate (or better) when compared with other sampling techniques using the same number of sampling points.

Robotics and adaptive sampling techniques for harbor waters monitoring: the MATRAC-ACP project

2019 ◽  
Author(s):  
Massimo Caccia ◽  
Marino Vetuschi Zuccolini ◽  
Lorenzo Brignone ◽  
Roberta Ferretti ◽  
Angelo Odetti ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Sampling Techniques

Analysis of adaptive sampling techniques for underwater vehicles

2013 ◽  
Vol 35 (2-3) ◽  
pp. 111-122 ◽  
Author(s):  
Andres Mora ◽  
Colin Ho ◽  
Srikanth Saripalli
Keyword(s):  
Adaptive Sampling ◽  
Underwater Vehicles ◽  
Sampling Techniques

scholarly journals Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond

2016 ◽  
Vol 13 (118) ◽  
pp. 20151107 ◽  
Author(s):  
Paris Perdikaris ◽  
George Em Karniadakis
Keyword(s):  
Parameter Estimation ◽  
Adaptive Sampling ◽  
Three Dimensional ◽  
Elliptic Partial Differential Equation ◽  
Sampling Procedure ◽  
Response Surfaces ◽  
Bayesian Optimization ◽  
New Paradigm ◽  
Model Inversion ◽  
Outflow Boundary

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation.

Optimization of grounding grids by response surfaces and genetic algorithms

2003 ◽  
Vol 39 (3) ◽  
pp. 1301-1304 ◽  
Author(s):  
M. Caldora Costa ◽  
M. Leite Pereira ◽  
Y. Marechal ◽  
J. Coulomb ◽  
J.R. Cardoso
Keyword(s):  
Genetic Algorithms ◽  
Response Surfaces ◽  
Grounding Grids

Optimization design of rockoons based on improved sequential approximation optimization

2021 ◽  
pp. 095441002110086
Author(s):  
Jiaxin Li ◽  
Ke Peng ◽  
Wenjie Wang ◽  
Zeping Wu ◽  
Weihua Zhang
Keyword(s):  
Adaptive Sampling ◽  
Optimization Design ◽  
High Efficiency ◽  
Launch Vehicle ◽  
Response Surfaces ◽  
Parameters Optimization ◽  
Multidisciplinary Design ◽  
Approximation Accuracy ◽  
Sequential Approximation ◽  
Approximation Optimization

In this study, a multidisciplinary design optimization framework and detailed procedure based on improved sequential approximation optimization (SAO) and 3-degree-of-freedom trajectory simulation are proposed for conceptual design and parameters optimization of a rockoon (from rocket and balloon) system. A reliable and efficient strategy, which considers the approximation accuracy of the response surfaces in the sampling process, is proposed to reduce the evaluation times of the original model for finding the global optimal solution. Besides, a modified SAO algorithm based on the multistage adaptive sampling strategy is presented, and the obtained tested results verify the fine robustness, high efficiency, reliability, and validity of the proposed SAO algorithm. The objective is to minimize the liftoff gross mass of the launch vehicle which reflects the cost for satellite launching. Constraints are imposed to ensure the orbit injection accuracy and stability of the launch vehicle. Finally, based on the multidisciplinary design framework with modified SAO, the optimal design results in 12 design cases from various payload mass, objective orbit, and ignition altitude are discussed and compared with the generic launch vehicle.

Techniques for Sampling Online Text-Based Data Sets

Big Data ◽  
2016 ◽  
pp. 655-675
Author(s):  
Lynne M. Webb ◽  
Yuanxin Wang
Keyword(s):  
Big Data ◽  
Social Networking ◽  
Adaptive Sampling ◽  
Online Gaming ◽  
Online Media ◽  
Sampling Techniques ◽  
Data Sets ◽  
Online Data ◽  
Social Networking Websites ◽  
Report Analysis

The chapter reviews traditional sampling techniques and suggests adaptations relevant to big data studies of text downloaded from online media such as email messages, online gaming, blogs, micro-blogs (e.g., Twitter), and social networking websites (e.g., Facebook). The authors review methods of probability, purposeful, and adaptive sampling of online data. They illustrate the use of these sampling techniques via published studies that report analysis of online text.

scholarly journals Evaluation of single and two-stage adaptive sampling designs for estimation of density and abundance of freshwater mussels in a large river

2011 ◽  
Vol 27 (1) ◽  
pp. 122-133 ◽  
Author(s):  
D. R. Smith ◽  
J. T. Rogala ◽  
B. R. Gray ◽  
S. J. Zigler ◽  
T. J. Newton
Keyword(s):  
Adaptive Sampling ◽  
Freshwater Mussels ◽  
Large River ◽  
Two Stage
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