Adaptive sampling techniques for surrogate modeling to create high-dimension aerodynamic loading response surfaces

2018 ◽  
Author(s):  
Andrew L. Kaminsky ◽  
Yi Wang ◽  
Kapil Pant ◽  
Wendy N. Hashii ◽  
Abraham Atachbarian
Keyword(s):  
High Dimension ◽  
Adaptive Sampling ◽  
Surrogate Modeling ◽  
Response Surfaces ◽  
Sampling Techniques ◽  
Aerodynamic Loading

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.

scholarly journals ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5332
Author(s):  
Carlos A. Duchanoy ◽  
Hiram Calvo ◽  
Marco A. Moreno-Armendáriz
Keyword(s):  
Artificial Intelligence ◽  
Model Selection ◽  
Adaptive Sampling ◽  
Search Algorithm ◽  
Sequential Sampling ◽  
Surrogate Modeling ◽  
Kriging Model ◽  
Surrogate Models ◽  
Physical Experiments ◽  
Leave One Out

Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

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

Surrogate Modeling of Complex Systems Using Adaptive Hybrid Functions

2011 ◽  
Author(s):  
Jie Zhang ◽  
Souma Chowdhury ◽  
Achille Messac ◽  
Junqiang Zhang ◽  
Luciano Castillo
Keyword(s):  
Radial Basis Functions ◽  
Wind Farm ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Modeling Method ◽  
Basis Functions ◽  
Sampling Techniques ◽  
Hybrid Functions ◽  
Model Complex ◽  
Radial Basis

This paper explores the effectiveness of the recently developed surrogate modeling method, the Adaptive Hybrid Functions (AHF), through its application to complex engineered systems design. The AHF is a hybrid surrogate modeling method that seeks to exploit the advantages of each component surrogate. In this paper, the AHF integrates three component surrogate models: (i) the Radial Basis Functions (RBF), (ii) the Extended Radial Basis Functions (E-RBF), and (iii) the Kriging model, by characterizing and evaluating the local measure of accuracy of each model. The AHF is applied to model complex engineering systems and an economic system, namely: (i) wind farm design; (ii) product family design (for universal electric motors); (iii) three-pane window design; and (iv) onshore wind farm cost estimation. We use three differing sampling techniques to investigate their influence on the quality of the resulting surrogates. These sampling techniques are (i) Latin Hypercube Sampling (LHS), (ii) Sobol’s quasirandom sequence, and (iii) Hammersley Sequence Sampling (HSS). Cross-validation is used to evaluate the accuracy of the resulting surrogate models. As expected, the accuracy of the surrogate model was found to improve with increase in the sample size. We also observed that, the Sobol’s and the LHS sampling techniques performed better in the case of high-dimensional problems, whereas the HSS sampling technique performed better in the case of low-dimensional problems. Overall, the AHF method was observed to provide acceptable-to-high accuracy in representing complex design systems.

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.

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