Online courses directory (15)
This is an introduction to the physics of atmospheric radiation and remote sensing including use of computer codes. Subjects covered include: radiative transfer equation including emission and scattering, spectroscopy, Mie theory, and numerical solutions. We examine the solution of inverse problems in remote sensing of atmospheric temperature and composition.
This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations; free energy and phase transitions; fluctuations and transport properties; and coarse-graining approaches and mesoscale models. The course employs case studies from industrial applications of advanced materials to nanotechnology. Several laboratories will give students direct experience with simulations of classical force fields, electronic-structure approaches, molecular dynamics, and Monte Carlo.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5107 (Atomistic Computer Modeling of Materials).
Support for this course has come from the National Science Foundation's Division of Materials Research (grant DMR-0304019) and from the Singapore-MIT Alliance.
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 includes: linear and finite deformation elasticity, inelasticity and dynamics. Numerical formulation and algorithms include: variational formulation and variational constitutive updates, finite element discretization, error estimation, constrained problems, time integration algorithms and convergence analysis. There is a strong emphasis on the (parallel) computer implementation of algorithms in programming assignments. The application to real engineering applications and problems in engineering science is stressed throughout the course.
This course provides students with an opportunity to conceive, design and implement a product, using rapid prototyping methods and computer-aid tools. The first of two phases challenges each student team to meet a set of design requirements and constraints for a structural component. A course of iteration, fabrication, and validation completes this manual design cycle. During the second phase, each team conducts design optimization using structural analysis software, with their phase one prototype as a baseline.
This course is made possible thanks to a grant by the alumni sponsored Teaching and Education Enhancement Program (Class of '51 Fund for Excellence in Education, Class of '55 Fund for Excellence in Teaching, Class of '72 Fund for Educational Innovation). The instructors gratefully acknowledge the financial support. The course was approved by the Undergraduate Committee of the MIT Department of Aeronautics and Astronautics in 2003. The instructors thank Prof. Manuel Martinez-Sanchez and the committee members for their support and suggestions.
CAD, or computer-aided design, is a powerful modeling tool that technical professionals use. With CAD, architects can draw up building plans and engineers can develop component and system designs. Some CAD programs even allow users to perform stress analysis, demonstrating how well a proposed structure will fare when put to use. For example, when does a load become too big? How much weight can be put onto a bridge before it becomes structurally unsound? Using CAD, professionals can create precise engineering drawings in both 2- and 3-D, complete with dimensions and specifications, in a neat and readable format. This modeling method has taken design to a whole new level of efficiency and accuracy. We are fortunate to be engineers working in the current eraone of computers, technology, and ease of precision. Without CAD, we would have to draft (or draw up) design blueprints by hand, which can be tedious and time-consuming. With CAD, however, we can generate accurate 2-D and 3-D drawings, scale…
Numerical methods have been used to solve mathematical expressions of engineering and scientific problems for at least 4000 years (for some historical discussion you may wish to browse the Ethnomathematics Digital Library  or the MacTutor History of Mathematics Archive  from St. Andrews University).* Such methods apply numerical approximation in order to convert continuous mathematical problems (for example, determining the mechanical stress throughout a loaded truss) into systems of discrete equations that can be solved with sufficient accuracy by machine. Numerical methods provide a way for the engineer to translate the language of mathematics and physics into information that may be used to make engineering decisions. Often, this translation is implemented so that calculations may be done by machines (computers). The types of problems that you encounter as an engineer may involve a wide variety of mathematical phenomena, and hence it will benefit you to have an equally wide range of numerical met…
Most mechanical engineering systems today involve significant amounts of electrical and electronic control systems. Effectively, most modern mechanical engineering systems are mechatronic systems. Mechatronics is the discipline that results from the synergetic application of electrical, electronic, computer, and control engineering in mechanical engineering systems. Thus, it is essential for the mechanical engineer to have a strong understanding of the composition and design of mechatronic systems, which is the goal of this course. Mechatronic systems are around us everywhere. A car contains many mechatronic systems, such as anti-lock braking systems, traction control, the engine control unit and cruise control, to name a few. A satellite dish position control unit is another example of a mechatronic system. Modern industrial automated processes would not be possible without the discipline of mechatronics, covering areas such as vehicle manufacturing, pharmaceutical industries, and food processing plants. R…
Engineering design is the process of creating solutions to satisfy certain requirements given all the constraints. This course will focus on the decision-making process that affects various stages of design, including resource allocation, scheduling, facilities management, material procurement, inspection, and quality control. You will be introduced to the basic theoretical framework and several practical tools you can use to support decision making in the future. The first two units provide an overview of engineering design process and theories and methods for making decisions, including Analytic Hierarchy Process, Lean Six Sigma, and Quality Function Deployment. In Unit 3, you will learn about the basic principles of computerized decision support systems. Unit 4 discusses several advanced mathematical methods used for support decision making, including linear and dynamic programming, decision tree, and Bayesian inference.
Nanotechnology is an emerging area that engages almost every technical discipline – from chemistry to computer science – in the study and application of extremely tiny materials. This short course allows any technically savvy person to go one layer beyond the surface of this broad topic to see the real substance behind the very small.
This short course provides an introduction to reactor dynamics including subcritical multiplication, critical operation in absence of thermal feedback effects and effects of Xenon, fuel and moderator temperature, etc. Topics include the derivation of point kinetics and dynamic period equations; techniques for reactor control including signal validation, supervisory algorithms, model-based trajectory tracking, and rule-based control; and an overview of light-water reactor startup. Lectures and demonstrations employ computer simulation and the use of the MIT Research Reactor.
This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.
Quantum computation is a remarkable subject building on the great computational discovery that computers based on quantum mechanics are exponentially powerful. This course aims to make this cutting-edge material broadly accessible to undergraduate students, including computer science majors who do not have any prior exposure to quantum mechanics. The course starts with a simple introduction to the fundamental principles of quantum mechanics using the concepts of qubits (or quantum bits) and quantum gates. This treatment emphasizes the paradoxical nature of the subject, including entanglement, non-local correlations, the no-cloning theorem and quantum teleportation. The course covers the fundamentals of quantum algorithms, including the quantum fourier transform, period finding, Shor's quantum algorithm for factoring integers, as well as the prospects for quantum algorithms for NP-complete problems. It also discusses the basic ideas behind the experimental realization of quantum computers, including the prospects for adiabatic quantum optimization and the D-Wave controversy.
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Do I need a textbook for this class?
No. Notes will be posted each week. If you wish to consult other references, a list of related textbooks and online resources will be provided.
What is the estimated effort for course?
About 5-12 hrs/week.
Why is the work load range so wide?
How long you spend on the course depends upon your background and on the depth to which you wish to understand the material. The topics in this course are quite open ended, and will be presented so you can understand them at a high level or can try to follow it at a sophisticated level with the help of the posted notes.
How much does it cost to take the course?
Nothing! The course is free.
Will the text of the lectures be available?
Yes. All of our lectures will have transcripts synced to the videos.
Do I need to watch the lectures live?
No. You can watch the lectures at your leisure.
This is an introduction to quantum computation, a cutting edge field that tries to exploit the exponential power of computers based on quantum mechanics. The course does not assume any prior background in quantum mechanics, and can be viewed as a very simple and conceptual introduction to that field.
This course provides an in-depth technical and policy analysis of various options for the nuclear fuel cycle. Topics include uranium supply, enrichment fuel fabrication, in-core physics and fuel management of uranium, thorium and other fuel types, reprocessing and waste disposal. Also covered are the principles of fuel cycle economics and the applied reactor physics of both contemporary and proposed thermal and fast reactors. Nonproliferation aspects, disposal of excess weapons plutonium, and transmutation of actinides and selected fission products in spent fuel are examined. Several state-of-the-art computer programs are provided for student use in problem sets and term papers.
The basic objective of Unified Engineering is to give a solid understanding of the fundamental disciplines of aerospace engineering, as well as their interrelationships and applications. These disciplines are Materials and Structures (M); Computers and Programming (C); Fluid Mechanics (F); Thermodynamics (T); Propulsion (P); and Signals and Systems (S). In choosing to teach these subjects in a unified manner, the instructors seek to explain the common intellectual threads in these disciplines, as well as their combined application to solve engineering Systems Problems (SP). Throughout the year, the instructors emphasize the connections among the disciplines.
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