Surrogate models for sheet metal stamping problem based on the combination of proper orthogonal decomposition and radial basis function

2017 ◽  
Author(s):  
Van Tuan Dang ◽  
Pascal Lafon ◽  
Carl Labergere
Keyword(s):  
Radial Basis Function ◽  
Proper Orthogonal Decomposition ◽  
Sheet Metal ◽  
Basis Function ◽  
Surrogate Models ◽  
Orthogonal Decomposition ◽  
Sheet Metal Stamping ◽  
Metal Stamping ◽  
Radial Basis ◽  
Proper Orthogonal

scholarly journals The Rapid Data-Driven Prediction Method of Coupled Fluid–Thermal–Structure for Hypersonic Vehicles

Aerospace ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 265
Author(s):  
Jing Liu ◽  
Meng Wang ◽  
Shu Li
Keyword(s):  
Radial Basis Function ◽  
Proper Orthogonal Decomposition ◽  
Basis Function ◽  
Thermal Structure ◽  
Prediction Method ◽  
Orthogonal Decomposition ◽  
Data Driven ◽  
Good Efficiency ◽  
Radial Basis ◽  
Proper Orthogonal

This work demonstrates the use of Latin Hypercube Sampling and Proper Orthogonal Decomposition in combination with a Radial Basis Function model to perform on vehicle prediction coupled fluid–thermal–structure. We regarded the Mach number, flight altitude and angle of attack as input parameters and established a rapid prediction model. The basic process of numerical simulation of the hypersonic vehicle coupled fluid–thermal–structure was studied to obtain the database of pressure coefficient, heat flux, structural temperature and structural stress as the sample data to train this prediction method. The prediction error was analyzed. The prediction results showed that the data-driven method proposed in this paper based on proper orthogonal decomposition and radial basis function could be used for predicting vehicle coupled fluid–thermal–structure with good efficiency.

scholarly journals Proper Orthogonal Decomposition–Radial Basis Function Surrogate Model-Based Inverse Analysis for Identifying Nonlinear Burgers Model Parameters From Nanoindentation Data

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Salah U. Hamim ◽  
Raman P. Singh
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Radial Basis Function ◽  
Proper Orthogonal Decomposition ◽  
Basis Function ◽  
Surrogate Model ◽  
Orthogonal Decomposition ◽  
Model Parameters ◽  
Radial Basis ◽  
Proper Orthogonal

This study explores the application of a proper orthogonal decomposition (POD) and radial basis function (RBF)-based surrogate model to identify the parameters of a nonlinear viscoelastic material model using nanoindentation data. The inverse problem is solved by reducing the difference between finite element simulation-trained surrogate model approximation and experimental data through genetic algorithm (GA)-based optimization. The surrogate model, created using POD–RBF, is trained using finite element (FE) data obtained by varying model parameters within a parametric space. Sensitivity of the model parameters toward the load–displacement output is utilized to reduce the number of training points required for surrogate model training. The effect of friction on simulated load–displacement data is also analyzed. For the obtained model parameter set, the simulated output matches well with experimental data for various experimental conditions.

scholarly journals Integration of expert knowledge into radial basis function surrogate models

2015 ◽  
Vol 17 (3) ◽  
pp. 577-603 ◽  
Author(s):  
Zuzana Nedělková ◽  
Peter Lindroth ◽  
Ann-Brith Strömberg ◽  
Michael Patriksson
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Expert Knowledge ◽  
Surrogate Models ◽  
Radial Basis

scholarly journals Sequential Optimization of Strip Bending Process Using Multiquadric Radial Basis Function Surrogate Models

2013 ◽  
Vol 554-557 ◽  
pp. 911-918 ◽  
Author(s):  
Jos Havinga ◽  
Gerrit Klaseboer ◽  
A.H. van den Boogaard
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Surrogate Model ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Sequential Optimization ◽  
Global Response ◽  
Local Point ◽  
Bending Process ◽  
Radial Basis

Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region. One of the popular surrogate modeling methods for SI is Kriging. However, the global response of Kriging models deteriorates in some cases due to local model refinement within SI. This may be problematic for multimodal optimization problems and for other applications where correct prediction of the global response is needed. In this paper the deteriorating global behavior of the Kriging surrogate modeling technique is shown for a model of a strip bending process. It is shown that a Radial Basis Function (RBF) surrogate model with Multiquadric (MQ) basis functions performs equally well in terms of optimization efficiency and better in terms of global predictive accuracy. The local point density is taken into account in the model formulation.

On the Potential of a Multi-Fidelity G-POD Based Approach for Optimization and Uncertainty Quantification

2014 ◽  
Author(s):  
David J. J. Toal
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Uncertainty Quantification ◽  
Surrogate Models ◽  
Orthogonal Decomposition ◽  
High Fidelity ◽  
Computational Domain ◽  
Surrogate Modelling ◽  
Proper Orthogonal ◽  
Fidelity Model ◽  
Entire Computational Domain

Traditional multi-fidelity surrogate models require that the output of the low fidelity model be reasonably well correlated with the high fidelity model and will only predict scalar responses. The following paper explores the potential of a novel multi-fidelity surrogate modelling scheme employing Gappy Proper Orthogonal Decomposition (G-POD) which is demonstrated to accurately predict the response of the entire computational domain thus improving optimization and uncertainty quantification performance over both traditional single and multi-fidelity surrogate modelling schemes.

scholarly journals Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models

2021 ◽  
Vol 26 (2) ◽  
pp. 31
Author(s):  
Manuel Berkemeier ◽  
Sebastian Peitz
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Optimization Problems ◽  
Trust Region ◽  
Surrogate Models ◽  
Descent Method ◽  
Information Need ◽  
Derivative Free ◽  
Radial Basis ◽  
General Scalar

We present a local trust region descent algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive problems, i.e., problems that have at least one objective function that is computationally expensive. Convergence to a Pareto critical point is proven. The method is derivative-free in the sense that derivative information need not be available for the expensive objectives. Instead, a multiobjective trust region approach is used that works similarly to its well-known scalar counterparts and complements multiobjective line-search algorithms. Local surrogate models constructed from evaluation data of the true objective functions are employed to compute possible descent directions. In contrast to existing multiobjective trust region algorithms, these surrogates are not polynomial but carefully constructed radial basis function networks. This has the important advantage that the number of data points needed per iteration scales linearly with the decision space dimension. The local models qualify as fully linear and the corresponding general scalar framework is adapted for problems with multiple objectives.

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