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Using Machine Learning as a Surrogate Model for Agent-Based Simulations
Claudio Angione
, Eric Silverman
, Elisabeth Yaneske
Centre for Digital Innovation
Department of Computing & Games
School of Computing, Engineering & Digital Technologies
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Article
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peer-review
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Keyphrases
Machine Learning
100%
Agent-based Simulation
100%
Surrogate Model
100%
Agent-based Model
66%
Machine Learning Techniques
50%
Artificial Neural Network
33%
Agent-based Modeling
33%
CPU Time
33%
Multiple Machine Learning
33%
Computational Model
16%
Training Time
16%
Detailed Model
16%
Statistical Model
16%
Monte Carlo Method
16%
Complex Behavior
16%
Time Use
16%
Accurate Model
16%
Robust Sensitivity Analysis
16%
Model Replication
16%
Model Surrogates
16%
Gaussian Process Surrogate
16%
Conceptual Work
16%
Gradient Boosted Trees
16%
Neural Gradient
16%
Chemical Engineering
Learning System
100%
Neural Network
50%
Computer Simulation
25%
Computer Science
Gradient Boosting Tree
25%
Earth and Planetary Sciences
Chaos
25%