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Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods [PDF]
Data Science and Engineering, 2022 Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and computer-assisted engineering.
Chun Kit Jeffery Hou +2 more
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Higher-order factorization machine for accurate surrogate modeling in material design [PDF]
Scientific ReportsEfficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to ...
Sanghyo Hwang +4 more
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Pre-training method in the tasks of obtaining surrogate models of gas turbine units for gas turbine electric power stations [PDF]
E3S Web of Conferences, 2023 This article focuses on the application of pre-training methods in the task of synthesizing surrogate models. The article emphasizes that pre-training significantly improves the accuracy of surrogate models and speeds up their creation process.
Kilin Grigory +3 more
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Surrogate-Based EM Design of RF and Microwave Components: A Systematic Review of Workflow Roles, Inverse Design, Multifidelity, and Active Learning [PDF]
SensorsSurrogate models have been increasingly used to reduce the computational cost of electromagnetic (EM) design in RF and microwave components. However, component types, surrogate model families, and design workflows vary substantially across the literature.
Maria Prousali, Stelios Tsitsos
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A surrogate FRAX model for Pakistan [PDF]
Archives of Osteoporosis, 2021 Abstract Summary A surrogate FRAX® model for Pakistan has been constructed using age-specific hip fracture rates for Indians living in Singapore and age-specific mortality rates from Pakistan.
Naureen, G. +11 more
openaire +6 more sources
Spatio-Temporal Gradient Enhanced Surrogate Modeling Strategies
Mathematical and Computational Applications, 2023 This research compares the performance of space-time surrogate models (STSMs) and network surrogate models (NSMs). Specifically, when the system response varies over time (or pseudo-time), the surrogates must predict the system response.
Johann M. Bouwer +2 more
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An artificial-neural-network-based surrogate modeling workflow for reactive transport modeling
Petroleum Research, 2022 Process-based reactive transport modeling (RTM) integrates thermodynamic and kinetically controlled fluid-rock interactions with fluid flow through porous media in the subsurface and surface environment.
Yupeng Li, Peng Lu, Guoyin Zhang
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Water Resources Research, 2023 Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial to characterize underground geologic properties and reduce prediction uncertainty.
Nanzhe Wang +2 more
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Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks
IEEE Access, 2020 Machine learning and artificial neural networks have shown to be applicable in modeling and simulation of complex physical phenomena as well as creating surrogate models trained with physics-based simulation data for numerous applications that require ...
Mikko Tahkola +4 more
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Modelling, 2023 Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization.
Corey Arndt +4 more
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