Empirical model-building and response surface
Empirical model-building and response surface
Response surface methodology: 1966–1988
Technometrics
Neural network models in simulation: a comparison with traditional modeling approaches
WSC '89 Proceedings of the 21st conference on Winter simulation
Prediction and prescription in systems modeling
Operations Research
Current applications, trends, and organizations in U.S. military simulation and gaming
Simulation and Gaming - Special issue on military simulation/gaming, part 1
Families of models that cross levels of resolution: issues for design, calibration and management
WSC '93 Proceedings of the 25th conference on Winter simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Techniques for simulation response optimization
Operations Research Letters
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Stochastic simulations of web search engines: RBF versus second-order regression models
Information Sciences—Informatics and Computer Science: An International Journal
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As a result of reduced budgets and personnel levels, the Department of Defense has increased reliance on combat simulations for such diverse areas as training, testing, planning, and analysis. Each area has its own set of needs, goals, and objectives for designing future generations of combat simulation models. However, budget constraints alone mandate the development of multipleuse combat models. The bottom line is that future generations of combat models need to be faster, have higher fidelity and larger scale than current models. Research into emerging technologies for approaches to make computer simulations more effective and efficient is an essential ingredient to developing successful future generations of combat models. One emerging technology that has such potential is Artificial Neural Networks (ANN). Potential applications of ANN to combat simulation modeling are discussed. The main results of the author's dissertation Artificial Neural Network Metamodels of Stochastic Computer Simulations [1] are discussed along with the ramifications on combat modeling and recommendations for future research.