Covers principles and methods for technical System Architecture. Presents a synthetic view including: the resolution of ambiguity to identify system goals and boundaries; the creative process of mapping form to function; the analysis of complexity and methods of decomposition and re-integration. Industrial speakers and faculty present examples from various industries. Heuristic and formal methods are presented. Restricted to SDM students.

Introduction to axiomatic design. Theoretical basis for rational design. One-FR Design. Multi-FR design. System design. Software design. Product design. Materials and materials process design. Manufacturing system design. Complexities in design: time-independent real complexity, time-independent imaginary complexity, time-dependent combinatorial complexity, and time-dependent periodic complexity. Industrial case studies. This course studies what makes a good design and how one develops a good design. Students consider how the design of engineered systems (such as hardware, software, materials, and manufacturing systems) differ from the "design" of natural systems such as biological systems; discuss complexity and how one makes use of complexity theory to improve design; and discover how one uses axiomatic design theory (AD theory) in design of many different kinds of engineered systems. Questions are analyzed using Axiomatic Design Theory and Complexity Theory. Case studies are presented including the design of machines, tribological systems, materials, manufacturing systems, and recent inventions. Implications of AD and complexity theories on biological systems discussed.

Mathematical models of systems from observations of their behavior. Time series, state-space, and input-output models. Model structures, parametrization, and identifiability. Non-parametric methods. Prediction error methods for parameter estimation, convergence, consistency, andasymptotic distribution. Relations to maximum likelihood estimation. Recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; and bounded but unknown noise models. Robustness and practical issues.

Subject focuses on project management principles, methods, and tools to effectively plan and implement successful developments. Material is divided into five major sections: project organization, planning, monitoring, control, and learning. Emphasis on new methodologies and tools such as deterministic, probabilistic and resource management, as well as project system dynamics and measurements. Topics are covered from strategic, tactical, and operational perspectives. Term projects analyze and evaluate past and ongoing projects in student's area of interest. Projects used to apply concepts discussed in class.

Introduction to quantitative methods and modeling techniques to address key questions in modern biology. Overview of quantitative modeling techniques in evolutionary biology, molecular biology and genetics, cell biology and developmental biology. Description of key experiments that validate models. Specific topics include: Evolutionary biology: theoretical models for evolution, evolution in test tube, evolution experiments with viruses and bacteria, complexity and evolution; Molecular biology and genetics: protein design, bioinformatics and genomics, constructing and modeling of genetic networks, control theory and genetic networks; Cell biology: forces and motion, cell motility, signal transduction pathways, chemotaxis and pheromone response; Development biology: pattern formation, self-organization, and models of Drosophila development.

In this seminar, we will discuss some of the main themes that have arisen in the field of systems biology, including the concepts of robustness, stochastic cell-to-cell variability, and the evolution of molecular interactions within complex networks.

This course covers introductory microbiology from a systems perspective, considering microbial diversity, population dynamics, and genomics. Emphasis is placed on the delicate balance between microbes and humans, and the changes that result in the emergence of infectious diseases and antimicrobial resistance. The case study approach covers such topics as vaccines, toxins, biodefense, and infections including Legionnaire‰ŰŞs disease, tuberculosis, Helicobacter pylori, and plague.

Upon successful completion of this course, students will be able to: * Create lumped parameter models (expressed as ODEs) of simple dynamic systems in the electrical and mechanical energy domains * Make quantitative estimates of model parameters from experimental measurements * Obtain the time-domain response of linear systems to initial conditions and/or common forcing functions (specifically; impulse, step and ramp input) by both analytical and computational methods * Obtain the frequency-domain response of linear systems to sinusoidal inputs * Compensate the transient response of dynamic systems using feedback techniques * Design, implement and test an active control system to achieve a desired performance measureMastery of these topics will be assessed via homework, quizzes/exams, and lab assignments.

A computational and application-oriented introduction to the modeling of large-scale systems in a wide variety of decision-making domains and the optimization of such systems using state-of-the-art optimization software. Application domains include transportation and logistics, pattern classification, structural design, financial engineering, and telecommunications system planning. Modeling tools and techniques covered include linear, network, discrete, and nonlinear programming, heuristic methods, sensitivity and postoptimality analysis, decomposition methods for large-scale systems, and stochastic programming.

Quantitative techniques for life cycle analysis of the impacts of materials extraction, processing use, and recycling; and economic analysis of materials processing, products, and markets. Student teams undertake a major case study of automobile manufacturing using the latest methods of analysis and computer-based models of materials process.

Introduces analysis techniques for complex structures and the role of material properties in structural design, failure, and longevity. Energy principles in structural analysis and applications to statically-indeterminate structures and solid continua. Matrix and finite-element methods of structured analysis including bars, beams, and two-dimensional plane stress elements. Structural materials and their properties; metals and composites. Modes of structural failure. Criteria for yielding and fracture. Crack formation and fracture mechanics. Fatigue and design for longevity. Other failure modes. Structural design project.

A graduate-level introduction to artificial intelligence. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning.

Technology Policy Negotiations and its prequel, ESD.932, Technology Policy Organizations, form a sequence on Organizational Processes in Technology Policy. This course provides a core framework for an interest-based approach to negotiations, along with a systems approach to dispute resolution in organizations. Core interactive skills are developed, including communication skills, negotiating over the "rules of game," and cross-cultural negotiations. Key assigments center on ethical debates in technology policy, regional economic development challenges, and assessment of organizational dispute resolution systems.

"In diesem Kurs erhalten Sie einen ?berblick ?ber einige wichtige literarische Texte, Tendenzen und Themen aus der deutschsprachigen Literatur- und Kulturszene. Wir werden literarische Texte, Gedichte, Theaterst?cke und Essays untersuchen, sowie andere ?sthetische Formen besprechen, wie Film und Architektur. Da alle Texte gleichzeitig in ihrem spezifischen kulturellen Kontext gelesen werden, tragen sie zu einem Verst?ndnis von verschiedenen historischen Aspekten bei. Unter anderen werden folgende Themen und Fragestellungen besprochen: Technologie und deren Einfluss auf die Gesellschaft, Fragen der Ethik bei wissenschaftlicher Arbeit, Konstruktion von nationaler Geschichte und kollektivem Ged?chtnis."

This course covers theories about the form that settlements should take and attempts a distinction between descriptive and normative theory by examining examples of various theories of city form over time. Case studies will highlight the origins of the modern city and theories about its emerging form, including the transformation of the nineteenth-century city and its organization. Through examples and historical context, current issues of city form in relation to city-making, social structure, and physical design will also be discussed and analyzed.

A more extensive and theoretical treatment of the material in 6.045J/18.400J, emphasizing computability and computational complexity theory. Regular and context-free languages. Decidable and undecidable problems, reducibility, recursive function theory. Time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems.

This course is an elementary introduction to number theory with no algebraic prerequisites. Topics covered include primes, congruences, quadratic reciprocity, diophantine equations, irrational numbers, continued fractions, and partitions.   

6.896ĺĘcovers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity.

6.895 covers theoretical foundations of general-purpose parallel computing systems, from languages to architecture. The focus is on the algorithmic underpinnings of parallel systems. The topics for the class will vary depending on student interest, but will likely include multithreading, synchronization, race detection, load balancing, memory consistency, routing networks, message-routing algorithms, and VLSI layout theory. The class will emphasize randomized algorithms and probabilistic analysis, including high-probability arguments.

This course covers topics such as sums of independent random variables, central limit phenomena, infinitely divisible laws, Levy processes, Brownian motion, conditioning, and martingales.