Using machine learning as a surrogate model for agent-based simulations. [PDF]
PLoS One, 2022 In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs).
Angione C, Silverman E, Yaneske E.
europepmc +2 more sources
Combined surrogate model for stress prediction of spherical bulkhead with openings
Zhongguo Jianchuan Yanjiu, 2022 ObjectiveA spherical bulkhead with openings has many structural parameters, and it is time-consuming and laborious to directly use the finite element method to carry out parameter research.
Tianyi CHEN +3 more
doaj +1 more source
Deep-learning-based coupled flow-geomechanics surrogate model for CO2 sequestration
International Journal of Greenhouse Gas Control, 2022 A deep-learning-based surrogate model capable of predicting flow and geomechanical responses in CO 2 storage operations is presented and applied. The 3D recurrent R-U-Net model combines deep convolutional and recurrent neural networks to capture the ...
Meng Tang, X. Ju, L. Durlofsky
semanticscholar +1 more source
Applied Energy, 2022 Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice.
Di Cao +6 more
semanticscholar +1 more source
Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems
Complex & Intelligent Systems, 2022 Many industrial applications require time-consuming and resource-intensive evaluations of suitable solutions within very limited time frames. Therefore, many surrogate-assisted evaluation algorithms (SAEAs) have been widely used to optimize expensive ...
Jie Tian +3 more
semanticscholar +1 more source
Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques [PDF]
Geoscientific Model Development, 2019 Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a
D. Lu, D. Ricciuto
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Training Meta-Surrogate Model for Transferable Adversarial Attack [PDF]
AAAI Conference on Artificial Intelligence, 2021 The problem of adversarial attacks to a black-box model when no queries are allowed has posed a great challenge to the community and has been extensively investigated.
Yunxiao Qin +3 more
semanticscholar +1 more source
Ensemble-Assisted Multi-Objective Optimization Algorithm Combining Feature Perturbation and Allocation Strategy [PDF]
Jisuanji gongcheng, 2022 A surrogate model can use its approximate prediction to replace the real evaluation of an algorithm when applied to the multi-objective optimization problem, which greatly reduces the number of real fitness evaluations required by the multi-objective ...
LIU Ziyi, WANG Yujia, SUN Fulu, JIA Xin, NIE Fangxin
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A machine learning-based surrogate model to approximate optimal building retrofit solutions
Applied Energy, 2021 The building sector has the highest share of operational energy consumption and greenhouse gas emissions among all sectors. Environmental targets set by many countries impose the need to improve the environmental footprint of the existing building stock.
E. Thrampoulidis +3 more
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The Creation of Surrogate Models for Fast Estimation of Complex Model Outcomes. [PDF]
PLoS ONE, 2016 A surrogate model is a black box model that reproduces the output of another more complex model at a single time point. This is to be distinguished from the method of surrogate data, used in time series.
W Andrew Pruett, Robert L Hester
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