CEE 4400

Artificial Intelligence for Control of Environmental Systems

Course Description

Course information provided by the 2026-2027 Catalog.

The course has the dual objective of introducing students to the control problems characterizing modern environmental infrastructures while exposing them to real-world examples of how modelling and control analytics are used. The course emphasizes the use of artificial intelligence techniques— particularly from reinforcement learning and approximate dynamic programming—alongside more traditional control methods. Specific topics include: (1) dynamic programming and its main extensions (stochastic and approximate DP), (2) policy search for single- and multi-objective problems, (3) deterministic and stochastic model predictive control, (4) model-order reduction and its application to large-scale control problems. Emphasis will be given not only to the theorical aspects underpinning the control problems, but also to their practical use. Examples will be drawn primarily from water-related domains—such as reservoir operations, urban water supply, and groundwater management—but will also include other environmental systems, such as forest fire suppression or ecosystem management, to illustrate the generalizability of the methods. Emphasis will be also placed on the role that data analysis plays in the formulation of these control problems. Class periods will be a mixture of in-person lectures and group discussions.

Prerequisites CEE 4665 and either CS 1110 or CS 1112.

Last 4 Terms Offered (None)

Learning Outcomes

• Identify the AI, modelling, and control analytics that are best suited for a given environmental system.
• Formulate and solve an optimal control problem using reinforcement learning or model-based approaches implemented in modern programming languages.
• Explain how modelling assumptions influence the performance of a control system.

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