Results 31 to 40 of about 22,296,575 (338)

Development of a diesel surrogate for improved autoignition prediction: Methodology and detailed chemical kinetic modeling

Applications in Energy and Combustion Science, 2023
While the surrogate fuel approach has been successfully applied to the simulation of the combustion behaviors of complex gasoline and jet fuels, its application to diesel fuels has been challenging.
Goutham Kukkadapu, Russell Whitesides, Mengyuan Wang, Scott W. Wagnon, Marco Mehl, Charles K. Westbrook, Robert McCormick, Chih-Jen Sung, William J. Pitz   +8 more
doaj   +1 more source

ML-Surrogate Model

This repository contains attachments related to the publication:**[Machine Learning Surrogate Optimization Framework for an Additively Manufactured, Linearly Polarized Metal Coaxial-to-Circular Waveguide Transition for Space Applications] ****Published in: ** [Engineering Applications of AI], [Volume xxx], [Issue yyy], [Year 2025], [Page Numbers zzz or
HASAN, MUHAMMAD FAHAD, Huang, He, Mushtaq, Ray Tahir   +2 more
  +4 more sources

A Kriging Surrogate Model for the Interference Reduction in the Settlement Surveillance Sensors of Steel Transmission Towers

Applied Sciences, 2019
The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly
Jiajia Shi, Liu Chu, Eduardo Souza de Cursi   +2 more
doaj   +1 more source

An Efficient Method for Complex Antenna Design Based on a Self Adaptive Surrogate Model-Assisted Optimization Technique

IEEE Transactions on Antennas and Propagation, 2020
Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern
Bo Liu, Mobayode O. Akinsolu, Chaoyun Song, Qiang Hua, Y. Huang, P. Excell, Muhammad Imran, Qian Xu   +7 more
semanticscholar   +1 more source

Development of surrogate-optimization models for a novel transcritical power cycle integrated with a small modular reactor

Energy and AI
In recent years, various types of surrogate optimization models have been proposed to reduce the computational time and to improve the emulation accuracy.
Yili Zhang, Jacob Bryan, Geordie Richards, Hailei Wang   +3 more
doaj   +1 more source

An efficient Bayesian method with intrusive homotopy surrogate model for stochastic model updating

Comput. Aided Civ. Infrastructure Eng.
This paper proposes a new stochastic model updating method based on the homotopy surrogate model (HSM) and Bayesian sampling. As a novel intrusive surrogate model, the HSM is established by the homotopy stochastic finite element (FE) method.
Hui Chen, Bin Huang, Heng Zhang, Kaiyi Xue, Ming Sun, Zhifeng Wu   +5 more
semanticscholar   +1 more source

Analytic Deep Learning-Based Surrogate Model for Operational Planning With Dynamic TTC Constraints [PDF]

IEEE Transactions on Power Systems, 2020
The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer capability (TTC) operational planning is unable to meet the real-time ...
Gao Qiu, You-bo Liu, Junbo Zhao, Junyong Liu, Lingfeng Wang, Tingjian Liu, Hongjun Gao   +6 more
semanticscholar   +1 more source

COMPARATIVE STUDY OF XAI USING FORMAL CONCEPT LATTICE AND LIME

ICTACT Journal on Soft Computing, 2022
Local Interpretable Model Agnostic Explanation (LIME) is a technique to explain a black box machine learning model using a surrogate model approach.
Bhaskaran Venkatsubramaniam, Pallav Kumar Baruah   +1 more
doaj   +1 more source

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model. [PDF]

Physical Review Letters, 2019
We propose a scheme for global optimization with first-principles energy expressions of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a number of local
M. K. Bisbo, B. Hammer
semanticscholar   +1 more source

Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error

, 2020
Bayesian inverse modeling is important for a better understanding of hydrological processes. However, this approach can be computationally demanding, as it usually requires a large number of model evaluations.
Chen, Dingjiang, Wu, Laosheng, Zeng, Lingzao, Zhang, Jiangjiang, Zheng, Qiang   +4 more
core   +1 more source