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www.reslog.polito.it
Exploring the synergies between AI and System Dynamics Modeling
keywords ARTIFICIAL INTELLIGENCE, LITERATURE REVIEW, MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS, MACHINE LEARNING, DEEP LEARNING, OPTIMIZATION, NEURAL NETWORKS, SIMULATION, SYSTEM DYNAMICS
Reference persons GIOVANNI ZENEZINI
External reference persons Filippo Maria Ottaviani
Research Groups www.reslog.polito.it
Thesis type RESEARCH, RESEARCH ORIENTED, RESEARCH THESIS
Description Organizations are increasingly leveraging the ability of artificial intelligence to analyze and resolve complex problems. This can potentially reshape the interdependencies and interactions of complex systems. While AI heavily relies on quantitative mathematical methods and data to extract “correlation” among variables in the system, System Dynamics (SD) is a modeling and simulation approach to elicit causal relationships between system variables. The two approaches can thus serve and complement each other well. This thesis will focus on developing a Systematic Literature review to determine which AI techniques work best in combination with system dynamics modelling and how.
Required skills Analytical skills
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Deadline 07/10/2025
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