surrogate models
Surrogate models for composed simulation models in energy systems
... appropriate surrogate models to represent the simulation models ...computer-based models, it can be assumed that samples can be easily generated and therefore one of the many available ...
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Weld sequence optimization: the use of surrogate models for solving sequential combinatorial problems
... simulations. Models that are cheaper representations of a more complex ones are referred to as surrogate models ...Because surrogate models are so specific about what they model most ...
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Towards domain-specific surrogate models for smart grid co-simulation
... Various applications of surrogate models in the energy domain can be found. Some of them do not use machine learning. Patsalides et al. presented a simplified distribution grid model (SDGM) to investigate ...
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Parametric Study & Development of Surrogate Models of Friction Stir Welding Process of Copper Plate
... A surrogate models can be used for optimization studies. It can be used to model the design objectives or to model the constraints. They are constructed to establish the relationship between the output ...
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An Optimization Method for an Aircraft Rear-end Conceptual Design Based on Surrogate Models
... Many documents have been written on multidisciplinary optimization in conceptual design and on the integration of surrogate models in this. Kroo et al. [1] decompose the problem into two levels: a system ...
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Optimization using surrogate models and partially converged computational fluid dynamics simulations
... of surrogate models 1 in optimization (Box & Draper 1987; Schonlau 1997; Jones 2001), which are used in lieu of direct calls to a CFD ...global surrogate model, of the drag of an aircraft for ...
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Generating physics based 1D surrogate models from 2D model results
... physics-based surrogate models have mostly gone unexplored (Fernández-Godino et ...a surrogate model as part of an uncertainty ...the surrogate model results to those of the original 2D ...
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Considerations of Accuracy and Uncertainty with Kriging Surrogate Models in Single Objective Electromagnetic Design Optimization
... yield surrogate models which approximate the true function to a high degree of accuracy; however, unless the true function being modelled is actually known and available for comparison, the true accuracy ...
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A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction
... This survey has given an overview over recent advances in the context of accelerating multiobjective optimization. These are surrogate models, feedback control and objective reduction techniques. Similar to ...
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Surrogate Models of Gravitational Waves from Numerical Relativity Simulations of Binary Black Hole Mergers
... waveform models will become increasingly stringent. NR surrogate models [14, 15, 76, 269] make no underlying assumptions about the structure of the waveform, and directly interpolate or fit NR data ...
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Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization
... of surrogate models for predicting the saltwater intrusion process in coastal ...rogate models is used in a stochastic multiobjective opti- mization using multiple-realization approach to derive ...
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High dimensional output surrogate models for uncertainty and sensitivity analyses
... Data-driven surrogate models are based on machine learning algorithms such as artificial neural networks (ANNs) [17, 18], Gaussian process (GP) or ‘Kriging’ models [19, 20], support and relevance ...
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Interpolatory Surrogate Models for Light-Induced Transition Dynamics.
... a surrogate model (patch) of the potential energy of the molecule in a local area around the current values of the design ...This surrogate is only accurate inside the domain from which it was ...the ...
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Uncertainty quantification in the assessment of seismic failure probability of nonstructural components using surrogate models
... approach for which the excitation is defined at the surface tends to quasi-systematically underestimate the risk of failure. To avoid this and knowing that in the non-linear case, the deconvolution problem is ...
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Genetic programming: efficient modeling tool in hydrology and groundwater management
... a surrogate model within the optimization algorithm as a substitute of the numerical simulation model in our study (Sreekanth and Datta, ...as surrogate models to replace groundwater numerical ...
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Stochastic approach for radionuclides quantification
... others, surrogate models available to simulate the gamma attenuation behaviour, a Bayesian approach which considers conditional probability densities of problem inputs, and Markov Chains Monte Carlo ...
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CAMELOT-Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox
... Multi-target missions using electric propulsion have been proposed in the literature [3, 22] and they are typical problems of the Global Trajectory Optimisation Competi- tion (GTOC) [32]. The design of such missions ...
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On the robust estimation of small failure probabilities for strong non-linear models
... IPM surrogate models either give an estimate of the uncertainty on the prediction of ˆ y or provide the analyst with a set-valued response that prescribes this ...the surrogate model on the ...
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CAMELOT - computational-analytical multi-fidelity low-thrust optimisation toolbox
... In this paper the Computational-Analytical Multi-fidelity Low-thrust Optimisation Toolbox (CAMELOT), a toolbox that combines the elements required to quickly design a low-thrust multi-target mission, is presented. ...
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New trends in optimization in electromagnetics
... Multi-objective methods using surrogate models may be divided into scalarizing and non-scalarizing. Scalarizing methods combine the multiple objectives of the MOOP using some function, and then use one of ...
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