1.008 Engineering Solutions to Societal Challenges [F19, S20, F20, F21]
Introduces societal-scale problems that span our built infrastructure and natural environment. Faculty members discuss case studies that highlight challenges and opportunities in the areas of smart cities, cyber-physical systems (transportation, electricity, and societal networks), sustainable resource management (land, water, and energy), and resilient design under the changing environment. Students study the use of data and computation in generating insights, and engage in practical laboratory sessions designed to promote critical thinking and problem-solving skills. Subject can count toward the 9-unit discovery-focused credit limit for first year students.
1.020 Engineering Sustainability: Analysis and Design [S21, S20, S19, S18]
Introduces a systems approach to modeling, analysis, and design of sustainable systems. Covers principles of dynamical systems, network models, optimization, and control, with applications in ecosystems, infrastructure networks, and energy systems. Includes a significant programming component. Students implement and analyze numerical models of systems, and make design decisions to balance physical, environmental, and economic considerations based on real and simulated data.
Building on core material in 6.402/482 (Modeling with Machine Learning: from Algorithms to Applications), emphasizes the design and operation of sustainable systems. Illustrates how to leverage heterogeneous data from urban services, cities, and the environment, and apply machine learning methods to evaluate and/or improve sustainability solutions. Provides case studies from various domains, such as transportation and urban mobility, energy and water resources, environmental monitoring, infrastructure sensing and control, climate adaptation, and disaster resilience. Projects focus on using machine learning to identify new insights or decisions that can help engineer sustainability in societal-scale systems. Students taking graduate version complete additional assignments. Students cannot receive credit without simultaneous completion of the core subject 6.402/482.
Introduction to statistical multivariate analysis methods and their applications to analyze data and mathematical models. Topics include sampling, experimental design, regression analysis, specification testing, dimension reduction, categorical data analysis, classification and clustering.
1.208 Resilient Networks [F20, F18, F17, F16]
Network and combinatorial optimization methods and game-theoretic modeling for resilience of large-scale networks against disruptions, both random and adversarial. Topics include network resilience metrics, interdiction and security games, strategic resource allocation and network design, cascades in networks, routing games and network equilibrium models, reliability and security assessment of networked systems, and incentive problems in network security. Applications to transportation, logistics, supply chain, communication, and electric power systems.
Participant teams of two play a strategic game, where each person plays a role of an attacker or defender of a simulated water network, and acts to disrupt or protect the network components while facing the opponent. A game-theoretic model will be utilized to compute the attacker and defender payoffs. Participants will see how their strategies performed compared to other teams. By conducting repeated plays of this game, the participants will learn how to make decisions in a constrained strategic environment.