A method using generative adversarial networks for robustness optimization

DOI10.1145/3503511zbMath1548.68214MaRDI QIDQ6638912

Soeren Bergmann, Florian Conrad, Niclas Feldkamp, Steffen Strassburger

Publication date: 14 November 2024

Published in: ACM Transactions on Modeling and Computer Simulation (Search for Journal in Brave)

zbMATH Keywords

machine learningdeep learninggenerative adversarial networksrobustness optimization

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Applications of game theory (91A80) Response surface designs (62K20) Robust parameter designs (62K25)

Cites Work

• Unnamed Item
• State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments
• Robust Optimization for Unconstrained Simulation-Based Problems
• An Introduction to Ranking and Selection
• Uniform Design: Theory and Application
• Constructing nearly orthogonal latin hypercubes for any nonsaturated run-variable combination
• Data farming: methods for the present, opportunities for the future
• Knowledge discovery in simulation data

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