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Sequential optimization of strip bending process using multiquadric radial basis function surrogate models [PDF]
, 2013 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.
Boogaard, A.H. van den +2 more
core +4 more sources
Water Resources Research, 2021 Surrogate models replace computationally expensive simulations of physically‐based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization ...
Alexander Garzón +3 more
semanticscholar +1 more source
Multifidelity Surrogate Models for Efficient Uncertainty Propagation Analysis in Salars Systems
Frontiers in Water, 2022 Salars are complex hydrogeological systems where the high-density contrasts require advanced numerical models to simulate groundwater flow and brine transport.
Vasileios Christelis, Andrew G. Hughes
doaj +1 more source
The SUMO toolbox: a tool for automatic regression modeling and active learning [PDF]
, 2013 Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alternative.
Couckuyt, Ivo +4 more
core +2 more sources
Optimal Design of Droplet Ejection for PZT Printhead Based on Surrogate Model
Applied Sciences, 2022 Droplet ejection technology is widely used in green and intelligent manufacturing. A stable jetting can be defined as no obvious satellite droplets during the whole ejection process, which is of great importance to ensure the quality and efficiency of ...
Ting Lei, Hong Liu, Cong Ma, Jiang Han
doaj +1 more source
Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients [PDF]
, 2020 In this paper, the multi-objective, multifidelity optimization of a wing fence on an unmanned aerial vehicle (UAV) near stall is presented. The UAV under consideration is characterized by a blended wing body (BWB), which increases its efficiency, and a ...
Couckuyt, Ivo +4 more
core +1 more source
On the use of gradients in Kriging surrogate models [PDF]
Proceedings of the Winter Simulation Conference 2014, 2014 The use of Kriging surrogate models has become popular in approximating computation-intensive deterministic computer models. In this work, the effect of enhancing Kriging surrogate models with a (partial) set of gradients is investigated. While, intuitively, gradient information is useful to enhance prediction accuracy, another motivation behind this ...
Selvakumar Ulaganathan +4 more
openaire +2 more sources
Universal Prediction Distribution for Surrogate Models [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2017 The use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions.
Ben Salem, Malek +3 more
openaire +5 more sources
IEEE AccessThe machine learning method for surrogate modeling is a keystone in surrogate model-assisted evolutionary algorithms (SAEAs). The current arguably most widely used surrogate modeling methods in SAEAs are Gaussian process and radial basis function.
Yushi Liu +3 more
doaj +1 more source
Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models [PDF]
, 2015 Simulating a binary black hole (BBH) coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time.
Blackman, Jonathan +6 more
core +2 more sources