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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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Efficient Surrogate Modeling and Design Optimization of Compact Integrated On-Chip Inductors Based on Multi-Fidelity EM Simulation Models [PDF]
Micromachines, 2021 High-performance and small-size on-chip inductors play a critical role in contemporary radio-frequency integrated circuits. This work presents a reliable surrogate modeling technique combining low-fidelity EM simulation models, response surface ...
Piotr Kurgan
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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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Development of surrogate models in reliability-based design optimization: A review
Mathematical Biosciences and Engineering, 2021 Reliability-based design optimization (RBDO) is applied to handle the unavoidable uncertainties in engineering applications. To alleviate the huge computational burden in reliability analysis and design optimization, surrogate models are introduced to ...
Xiaoke Li +6 more
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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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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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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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Two-stage variable-fidelity modeling of antennas with domain confinement
Scientific Reports, 2022 Surrogate modeling has become the method of choice in solving an increasing number of antenna design tasks, especially those involving expensive full-wave electromagnetic (EM) simulations. Notwithstanding, the curse of dimensionality considerably affects
Anna Pietrenko-Dabrowska +2 more
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