This course is designed for graduate students with an interest in using primary research literature to discuss and learn about current research around non-conventional light stable isotope geochemistry.

Principles of tomographic imaging using ionizing and non-ionizing radiation, and ultrasound. Emphasis is placed on fundamental physics and mathematics involved in image formation, including basic interactions, data acquisition and reconstruction. Planar radiographic imaging, multi-dimensional tomography (X-ray CAT, PET, SPECT), ultrasound, and NMR imaging covered. 22.56J aims to give graduate students and advanced undergraduates background in the theory and application of noninvasive imaging methods to biology and medicine, with emphasis on neuroimaging. The course focuses on the modalities most frequently used in scientific research (X-ray CT, PET/SPECT, MRI, and optical imaging), and includes discussion of molecular imaging approaches used in conjunction with these scanning methods. Lectures by the professor will be supplemented by in-class discussions of problems in research, and hands-on demonstrations of imaging systems.

Introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. Forced and parametric oscillators. Phase space. Periodic, quasiperiodic, and aperiodic flows. Sensitivity to initial conditions and strange attractors. Lorenz attractor. Period doubling, intermittency, and quasiperiodicity. Scaling and universality. Analysis of experimental data: Fourier transforms, Poincar, sections, fractal dimension, and Lyapunov exponents. Applications drawn from fluid dynamics, physics, geophysics, and chemistry.

This graduate-level course provides a unified treatment of nonlinear oscillations and wave phenomena with applications to mechanical, optical, geophysical, fluid, electrical and flow-structure interaction problems. A more detailed course outline is given in the syllabus section.

This course presents micro-econometric models, including large sample theory for estimation and hypothesis testing, generalized method of moments (GMM), estimation of censored and truncated specifications, quantile regression, structural estimation, nonparametric and semiparametric estimation, treatment effects, panel data, bootstrapping, simulation methods, and Bayesian methods. The methods are illustrated with economic applications

Introduction to reactor dynamics including subcritical multiplication, critical operation in absence of thermal feedback effects and effects of Xenon, fuel and moderator temperature, etc. 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. Overview of light-water reactor startup. Lectures and demonstrations with computer simulation and the use of the MIT Research Reactor.

Problems in nuclear engineering often involve applying knowledge from many disciplines simultaneously in achieving satisfactory solutions. The course will focus on understanding the complete nuclear reactor system including the balance of plant, support systems and resulting interdependencies affecting the overall safety of the plant and regulatory oversight. Both the Seabrook and Pilgrim nuclear plant simulators will be used as part of the educational experience to provide as realistic as possible understanding of nuclear power systems short of being at the reactor.

This capstone course is a group design project involving integration of nuclear physics, particle transport, control, heat transfer, safety, instrumentation, materials, environmental impact, and economic optimization. It provides opportunities to synthesize knowledge acquired in nuclear and non-nuclear subjects and apply this knowledge to practical problems of current interest in nuclear applications design. Each year, the class takes on a different design project; this year, the project is a power plant design that ties together the creation of emission-free electricity with carbon sequestration and fossil fuel displacement. Students taking graduate version complete additional assignments.This course is an elective subject in MIT's undergraduate 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.

This course will expose students to tools and methods of analysis for use in assessing the challenges and dangers associated with nuclear weapons in international politics. The first two weeks of the course will look at the technology and design of nuclear weapons and their means of production. The next five weeks will look at the role they played in the Cold War, the organizations that managed them, the technologies that were developed to deliver them, and the methods used to analyze nuclear force structures and model nuclear exchanges. The last six weeks of the course will look at theories and cases of nuclear decision making beyond the original five weapon states, and will look particularly at why states pursue or forego nuclear weapons, the role that individuals and institutions play, and the potential for both new sources of proliferation and new consequences.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB programming.

This course is an introduction to numerical methods and MATLAB®: Errors, condition numbers and roots of equations. Topics covered include Navier-Stokes; direct and iterative methods for linear systems; finite differences for elliptic, parabolic and hyperbolic equations; Fourier decomposition, error analysis and stability; high-order and compact finite-differences; finite volume methods; time marching methods; Navier-Stokes solvers; grid generation; finite volumes on complex geometries; finite element methods; spectral methods; boundary element and panel methods; turbulent flows; boundary layers; and Lagrangian coherent structures (LCSs).

Introduction to numerical methods: interpolation, differentiation, integration, systems of linear equations. Solution of differential equations by numerical integration, partial differential equations of inviscid hydrodynamics: finite difference methods, panel methods. Fast Fourier Transforms. Numerical representation of sea waves. Computation of the motions of ships in waves. Integral boundary layer equations and numerical solutions.

This graduate-level course is an advanced introduction to applications and theory of numerical methods for solution of differential equations. In particular, the course focuses on physically-arising partial differential equations, with emphasis on the fundamental ideas underlying various methods.

The nuts and bolts of preparing a New Venture Plan and launching the venture will be explored in this twenty-fifth annual course offering. The course is open to members of the MIT Community and to others interested in entrepreneurship. It is particularly recommended for persons who are interested in starting or are involved in a new business or venture. Because some of the speakers will be judges of the MIT $100K Entrepreneurship Competition, persons who are planning to enter the Competition should find the course particularly useful. In the past approximately 50% of the class has been from the Engineering / Science / Architecture Schools and 50% from the Sloan School of Management.

This course teaches the fundamentals of engineering operating systems. The following topics are studied in detail: virtual memory, kernel and user mode, system calls, threads, context switches, interrupts, interprocess communication, coordination of concurrent activities, and the interface between software and hardware. Most importantly, the interactions between these concepts are examined. The course is divided into two blocks; the first block introduces one operating system, UNIXĺ¨ v6, in detail. The second block of lectures covers important operating systems concepts invented after UNIXĺ¨ v6, which was introduced in 1976.

Our objective in this course is to introduce you to concepts and techniques related to the design, planning, control, and improvement of manufacturing and service operations. The course begins with a holistic view of operations, where we stress the coordination of product development, process management, and supply chain management. As the course progresses, we will investigate various aspects of each of these three tiers of operations in detail. We will cover topics in the areas of process analysis, materials management, production scheduling, quality improvement, and product design. To pursue the course objective most effectively, you will have to: 1. Study the assigned reading materials. 2. Prepare and discuss cases, readings, and exercises in class. 3. Prepare written analyses of cases.

Operations Strategy provides a unifying framework for analyzing strategic issues in manufacturing and service operations. Students analyze the relationships between manufacturing and service companies and their suppliers, customers, and competitors. The course covers strategic decisions in technology, facilities, vertical integration, human resources, and other areas, and also explores means of competition such as cost, quality, and innovativeness.

This course provides an introduction to optical science with elementary engineering applications. Topics covered in geometrical optics include: ray-tracing, aberrations, lens design, apertures and stops, radiometry and photometry. Topics covered in wave optics include: basic electrodynamics, polarization, interference, wave-guiding, Fresnel and Fraunhofer diffraction, image formation, resolution, space-bandwidth product. Analytical and numerical tools used in optical design are emphasized. Graduate students are required to complete assignments with stronger analytical content, and an advanced design project.

This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods.

Introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, dynamic programming, integer programming, non-linear programming, and heuristics. Applications to logistics, manufacturing, transportation, E-commerce, project management, and finance.