Results 11 to 20 of about 186,324 (294)
Pre-training method in the tasks of obtaining surrogate models of gas turbine units for gas turbine electric power stations [PDF]
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
doaj +1 more source
Higher-order factorization machine for accurate surrogate modeling in material design. [PDF]
Efficient 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 ...
Hwang S, Kim S, Xu Z, Luo T, Lee E.
europepmc +2 more sources
Spatio-Temporal Gradient Enhanced Surrogate Modeling Strategies
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
An artificial-neural-network-based surrogate modeling workflow for reactive transport modeling
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
doaj +1 more source
Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks
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
doaj +1 more source
Two-stage variable-fidelity modeling of antennas with domain confinement
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
doaj +1 more source
Cost-Efficient Bi-Layer Modeling of Antenna Input Characteristics Using Gradient Kriging Surrogates
Over the recent years, surrogate modeling has been playing an increasing role in the design of antenna structures. The main incentive is to mitigate the issues related to high cost of electromagnetic (EM)-based procedures.
Anna Pietrenko-Dabrowska +2 more
doaj +1 more source
Electrical machine design optimization is an expensive procedure as it contains numerous variables and multiple objectives. Therefore, it might require hundreds of time‐consuming finite element analyses (FEA).
Farnam Farshbaf Roomi +2 more
doaj +1 more source

