Results 31 to 40 of about 186,324 (294)
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
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Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs
MathematicsInverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations,
Xiaopeng Ma +4 more
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The Gaussian Process Modeling Module in UQLab [PDF]
Journal of Soft Computing in Civil Engineering, 2018 We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a ...
Christos Lataniotis +2 more
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Surrogate modeling based cognitive decision engine for optimization of WLAN performance [PDF]
, 2017 Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes
Chemmangat, Krishnan +9 more
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A novel adaptive-weight ensemble surrogate model base on distance and mixture error.
PLoS ONE, 2023 Surrogate models are commonly used as a substitute for the computation-intensive simulations in design optimization. However, building a high-accuracy surrogate model with limited samples remains a challenging task. In this paper, a novel adaptive-weight
Jun Lu, Yudong Fang, Weijian Han
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Pulsar Signal Adaptive Surrogate Modeling
AerospaceAs the number of spacecraft heading beyond Earth’s orbit increased in recent years, autonomous navigation solutions have become increasingly important. One such solution is pulsar-based navigation.
Tomáš Kašpárek, Peter Chudý
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A PINN Surrogate Modeling Methodology for Steady-State Integrated Thermofluid Systems Modeling
Mathematical and Computational Applications, 2023 Physics-informed neural networks (PINNs) were developed to overcome the limitations associated with the acquisition of large training data sets that are commonly encountered when using purely data-driven machine learning methods.
Kristina Laugksch +2 more
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Treatment Decision‐Making Roles and Preferences Among Adolescents and Young Adults With Cancer
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Decision‐making (DM) dynamics between adolescents and young adults (AYAs) with cancer, parents, and oncologists remain underexplored in diverse populations. We examined cancer treatment DM preferences among an ethnically and socioeconomically diverse group of AYAs and their parents.
Amanda M. Gutierrez +14 more
wiley +1 more source
Failure Control of an Electric Transmission Tower System under Strong Earthquakes
Eurasian Journal of Science and Engineering, 2022 The aim of the project is to control the failure of the structural system of an electric transmission tower system during strong earthquakes. Optimization process is being applied by the support of both surrogate modeling and Hessian Matrix method using ...
Nazim Abdul Nariman +3 more
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Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim [PDF]
, 2009 Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the ...
Deshpande, S. +4 more
core +3 more sources