Students learn about complex networks and how to represent them using graphs. They also learn that graph theory is a useful mathematical tool for studying complex networks in diverse applications of science and engineering, such as neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Topics covered include set theory, defining a graph, as well as defining the degree of a node and the degree distribution of a graph.

16.225 is a graduate level course on Computational Mechanics of Materials. The primary focus of this course is on the teaching of state-of-the-art numerical methods for the analysis of the nonlinear continuum response of materials. The range of material behavior considered in this course will include: linear and finite deformation elasticity, inelasticity and dynamics. Numerical formulation and algorithms will include: Variational formulation and variational constitutive updates, finite element discretization, error estimation, constrained problems, time integration algorithms and convergence analysis. There will be a strong emphasis on the (parallel) computer implementation of algorithms in programming assignments. At the beginning of the course, the students will be given the source of a base code with all the elements of a finite element program which constitute overhead and do not contribute to the learning objectives of this course (assembly and equation-solving methods, etc.). Each assignment will consist of formulating and implementing on this basic platform, the increasingly complex algorithms resulting from the theory given in class, as well as in using the code to numerically solve specific problems. The application to real engineering applications and problems in engineering science will be stressed throughout.

Covers computational and data analysis techniques for environmental engineering applications. First third of subject introduces MATLAB and numerical modeling. Second third emphasizes probabilistic concepts used in data analysis. Final third provides experience with statistical methods for analyzing field and laboratory data. Numerical techniques such as Monte Carlo simulation are used to illustrate the effects of variability and sampling. Concepts are illustrated with environmental examples and data sets. This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLABĺ¨ programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

Experiment with conductivity in metals, plastics and photoconductors. See why metals conduct and plastics don't, and why some materials conduct only when you shine a flashlight on them.

Students analyze video clips of kids rolling down a hill on skates, scooters, and bikes to determine whether mechanical energy is conserved.

Successful completion of this cooperative learning activity requires the active involvement of the individual, the small group and the entire classroom (collaboration). The goal is to make a simple task as complicated as possible by constructing a single complex machine.

Control Systems is an inter-disciplinary engineering text that analyzes the effects and interactions of mathematical systems. This book is for third and fourth year undergraduates in an engineering program.

The objective of this subject is to understand the nature of manufacturing process variation and the methods for its control. First, a general process model for control is developed to understand the limitations a specific process places on the type of control used. A general model for process variation is presented and three methods are developed to minimize variations: Statistical Process Control, Process Optimization and in-process Feedback Control. These are considered in a hierarchy of cost-performance tradeoffs, where performance is based on changes in process capability.

This lesson goes over some basic elements of engineering and the paper making process. We will be using the circumference of a circle and conversions to program motors to make sure the dryer cans are going at the correct RPM's to ensure the paper machine functions correctly. This is a lesson that can be self taught or can be teacher led.

This course will focus on fundamental subjects in convexity, duality, and convex optimization algorithms. The aim is to develop the core analytical and algorithmic issues of continuous optimization, duality, and saddle point theory using a handful of unifying principles that can be easily visualized and readily understood.

A brief refresher on the Cartesian plane includes how points are written in (x, y) format and oriented to the axes, and which directions are positive and negative. Then students learn about what it means for a relation to be a function and how to determine domain and range of a set of data points.

Students learn about the many types of expenses associated with building a bridge. Working like engineers, they estimate the cost for materials for a bridge member of varying sizes. After making calculations, they graph their results to compare how costs change depending on the use of different materials (steel vs. concrete). They conclude by creating a proposal for a city bridge design based on their findings.

In this lesson, students find their location on a map using Latitude and Longitudinal coordinates. They determine where they should go to be rescued and how best to get there.

This activity gives a visual representation of how we are able to observe many colors in a sunrise or sunset.

The subject of this course is the historical process by which the meaning of "technology" has been constructed. Although the word itself is traceable to the ancient Greek root teckhne (meaning art), it did not enter the English language until the 17th century, and did not acquire its current meaning until after World War I. The aim of the course, then, is to explore various sectors of industrializing 19th and 20th century Western society and culture with a view to explaining and assessing the emergence of technology as a pivotal word (and concept) in contemporary (especially Anglo-American) thought and expression.

Students gain experience with the software/system design process, closely related to the engineering design process, to solve a problem. First, they learn about the Mars Curiosity rover and its mission, including the difficulties that engineers must consider and overcome to operate a rover remotely. Students observe a simulation of a robot being controlled remotely. These experiences guide discussion on how the design process is applied in these scenarios. The lesson culminates in a hands-on experience with the design process as students simulate the remote control of a rover. In the associated activity, students gain further experience with the design process by creating an Android application using App Inventor to control one aspect of a remotely controlled vehicle. (Note: The lesson requires a LEGO® MINDSTORMS® Education NXT base set.)

This wiki page describes the DIY Design Challenge Activity used during ISKME's Teacher Academy. Design a DIY Project for students that meets the following design principles:The design must meet a need for their school community. The design must use collaboration between teacher, student, and community. The design must be multidisciplinary. The design must use low cost and/or no cost materials. Group examples are included.

D-Lab: Energy offers a hands-on, project-based approach that engages students in understanding and addressing the applications of small-scale, sustainable energy technology in developing countries where compact, robust, low-cost systems for generating power are required. Projects may include micro-hydro, solar, or wind turbine generators along with theoretical analysis, design, prototype construction, evaluation and implementation. Students will have the opportunity both to travel to Nicaragua during spring break to identify and implement projects. D-Lab: Energy is part of MIT’s D-Lab program, which fosters the development of appropriate technologies and sustainable solutions within the framework of international development. This course is an elective subject in MIT’s underGraduate / Professional Energy Studies Minor. This Institute-wide program complements the deep expertise obtained in any major with a broad understanding of the interlinked realms of science, technology, and social sciences as they relate to energy and associated environmental challenges.

D-Lab: Energy offers a hands-on, project-based approach that engages students in understanding and addressing the applications of small-scale, sustainable energy technology in developing countries where compact, robust, low-cost systems for generating power are required. Projects may include micro-hydro, solar, or wind turbine generators along with theoretical analysis, design, prototype construction, evaluation and implementation. Students will have the opportunity both to travel to Nicaragua during spring break to identify and implement projects. D-Lab: Energy is part of MIT's D-Lab program, which fosters the development of appropriate technologies and sustainable solutions within the framework of international development.

D-Lab Development addresses issues of technological improvements at the micro level for developing countries—in particular, how the quality of life of low-income households can be improved by adaptation of low cost and sustainable technologies. Discussion of development issues as well as project implementation challenges are addressed through lectures, case studies, guest speakers and laboratory exercises. Students form project teams to partner with mostly local level organizations in developing countries, and formulate plans for an IAP site visit. (Previous field sites include Ghana, Brazil, Honduras and India.) Project team meetings focus on developing specific projects and include cultural, social, political, environmental and economic overviews of the countries and localities to be visited as well as an introduction to the local languages.