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16.888 Multidisciplinary System Design Optimization (MIT) 16.888 Multidisciplinary System Design Optimization (MIT)

Description

This course is mainly focused on the quantitative aspects of design and presents a unifying framework called "Multidisciplinary System Design Optimization" (MSDO). The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context, focusing on three aspects of the problem: (i) The multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. There is a version of this course (16.60s) offered through the MIT Professional Institute, targeted at professional engineers. This course is mainly focused on the quantitative aspects of design and presents a unifying framework called "Multidisciplinary System Design Optimization" (MSDO). The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context, focusing on three aspects of the problem: (i) The multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. There is a version of this course (16.60s) offered through the MIT Professional Institute, targeted at professional engineers.

Subjects

optimization | optimization | multidisciplinary design optimization | multidisciplinary design optimization | MDO | MDO | subsystem identification | subsystem identification | interface design | interface design | linear constrained optimization fomulation | linear constrained optimization fomulation | non-linear constrained optimization formulation | non-linear constrained optimization formulation | scalar optimization | scalar optimization | vector optimization | vector optimization | systems engineering | systems engineering | complex systems | complex systems | heuristic search methods | heuristic search methods | tabu search | tabu search | simulated annealing | simulated annealing | genertic algorithms | genertic algorithms | sensitivity | sensitivity | tradeoff analysis | tradeoff analysis | goal programming | goal programming | isoperformance | isoperformance | pareto optimality | pareto optimality | flowchart | flowchart | design vector | design vector | simulation model | simulation model | objective vector | objective vector | input | input | discipline | discipline | output | output | coupling | coupling | multiobjective optimization | multiobjective optimization | optimization algorithms | optimization algorithms | tradespace exploration | tradespace exploration | numerical techniques | numerical techniques | direct methods | direct methods | penalty methods | penalty methods | heuristic techniques | heuristic techniques | SA | SA | GA | GA | approximation methods | approximation methods | sensitivity analysis | sensitivity analysis | isoperformace | isoperformace | output evaluation | output evaluation | MSDO framework | MSDO framework

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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ESD.70J Engineering Economy Module (MIT) ESD.70J Engineering Economy Module (MIT)

Description

This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 – Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools – such as Data Table and Goal Seek. It is also useful for a variety of other subjects.NoteThis MIT OpenCourseWare site is based on the materials from Professor de Neufville's ESD.70J Web site. This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 – Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools – such as Data Table and Goal Seek. It is also useful for a variety of other subjects.NoteThis MIT OpenCourseWare site is based on the materials from Professor de Neufville's ESD.70J Web site.

Subjects

excel | excel | spreadsheet | spreadsheet | modeling | modeling | dynamic modeling | dynamic modeling | analysis | analysis | data table | data table | goal seek | goal seek | sensitivity analysis | sensitivity analysis | simulation | simulation | random number generator | random number generator | counting | counting | modeling uncertainties | modeling uncertainties | random variables | random variables | statistical package | statistical package | flexibility | flexibility | contingency rules | contingency rules | excel solver | excel solver | solver | solver

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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15.066J System Optimization and Analysis for Manufacturing (MIT) 15.066J System Optimization and Analysis for Manufacturing (MIT)

Description

One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management. One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management.

Subjects

modeling | modeling | optimization | optimization | simulation | simulation | manufacturing systems | manufacturing systems | decision making | decision making | decision support | decision support | probabilistic simulation | probabilistic simulation | designing manufacturing systems | designing manufacturing systems | operations management | operations management | linear programming | linear programming | sensitivity analysis | sensitivity analysis | network flow problems | network flow problems | non-linear programming | non-linear programming | Lagrange multipliers | Lagrange multipliers | integer programming | integer programming | discrete-event simulation | discrete-event simulation | heuristics | heuristics | algorithms | algorithms | 15.066 | 15.066 | 2.851 | 2.851 | 3.83 | 3.83

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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ESD.70J Engineering Economy Module (MIT) ESD.70J Engineering Economy Module (MIT)

Description

This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools, such as Data Table and Goal Seek. It is also useful for a variety of other subjects. This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools, such as Data Table and Goal Seek. It is also useful for a variety of other subjects.

Subjects

ESD.70 | ESD.70 | 1.145 | 1.145 | excel | excel | spreadsheet | spreadsheet | modeling | modeling | dynamic modeling | dynamic modeling | analysis | analysis | data table | data table | goal seek | goal seek | sensitivity analysis | sensitivity analysis | simulation | simulation | random number generator | random number generator | counting | counting | modeling uncertainties | modeling uncertainties | random variables | random variables | statistical package | statistical package | flexibility | flexibility | contingency rules | contingency rules | excel solver | excel solver | solver | solver

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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HST.750 Modeling Issues in Speech and Hearing (MIT) HST.750 Modeling Issues in Speech and Hearing (MIT)

Description

This course explores the theory and practice of scientific modeling in the context of auditory and speech biophysics. Based on seminar-style discussions of the research literature, the class draws on examples from hearing and speech, and explores general, meta-theoretical issues that transcend the particular subject matter. Examples include: What is a model? What is the process of model building? What are the different approaches to modeling? What is the relationship between theory and experiment? How are models tested? What constitutes a good model? This course explores the theory and practice of scientific modeling in the context of auditory and speech biophysics. Based on seminar-style discussions of the research literature, the class draws on examples from hearing and speech, and explores general, meta-theoretical issues that transcend the particular subject matter. Examples include: What is a model? What is the process of model building? What are the different approaches to modeling? What is the relationship between theory and experiment? How are models tested? What constitutes a good model?

Subjects

hearing | hearing | speech | speech | modeling biology | modeling biology | network model of the ear | network model of the ear | model building | model building | dimensional analysis and scaling | dimensional analysis and scaling | resampling | resampling | monte carlo | monte carlo | forward vs. inverse | forward vs. inverse | chaos | chaos | limits of prediction | limits of prediction | hodgkin | hodgkin | huxley | huxley | molecular mathematic biology | molecular mathematic biology | cochlear input impedance | cochlear input impedance | auditory network | auditory network | auditory morphology | auditory morphology | electric model of neural cell fiber | electric model of neural cell fiber | electric diagrams of neural cells | electric diagrams of neural cells | linear regression | linear regression | sensitivity analysis | sensitivity analysis | cochlea | cochlea | inner ear | inner ear | middle ear | middle ear | auditory cortex | auditory cortex | scientific literature | scientific literature | analysis | analysis | paper analysis | paper analysis | tent maps | tent maps | quadratic maps | quadratic maps

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Counting the cost of effective health policy

Description

Part of a series of worksheets covering Mathematical Case Studies for Economists from Nottingham Trent University. They are downloadable in Word format with embedded links. They can be adapted, printed and/or put in a Virtual Learning Environment. A booklet giving guideline answers for the task questions is available on request from the Economics Network.

Subjects

ukoer | trueproject | economics | mathematics | marginal cost | sensitivity analysis | expectation | probability | Social studies | L000

License

Attribution-Noncommercial 2.0 UK: England & Wales Attribution-Noncommercial 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc/2.0/uk/ http://creativecommons.org/licenses/by-nc/2.0/uk/

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16.888 Multidisciplinary System Design Optimization (MIT)

Description

This course is mainly focused on the quantitative aspects of design and presents a unifying framework called "Multidisciplinary System Design Optimization" (MSDO). The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context, focusing on three aspects of the problem: (i) The multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. There is a version of this course (16.60s) offered through the MIT Professional Institute, targeted at professional engineers.

Subjects

optimization | multidisciplinary design optimization | MDO | subsystem identification | interface design | linear constrained optimization fomulation | non-linear constrained optimization formulation | scalar optimization | vector optimization | systems engineering | complex systems | heuristic search methods | tabu search | simulated annealing | genertic algorithms | sensitivity | tradeoff analysis | goal programming | isoperformance | pareto optimality | flowchart | design vector | simulation model | objective vector | input | discipline | output | coupling | multiobjective optimization | optimization algorithms | tradespace exploration | numerical techniques | direct methods | penalty methods | heuristic techniques | SA | GA | approximation methods | sensitivity analysis | isoperformace | output evaluation | MSDO framework

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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ESD.70J Engineering Economy Module (MIT)

Description

This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 – Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools – such as Data Table and Goal Seek. It is also useful for a variety of other subjects.NoteThis MIT OpenCourseWare site is based on the materials from Professor de Neufville's ESD.70J Web site.

Subjects

excel | spreadsheet | modeling | dynamic modeling | analysis | data table | goal seek | sensitivity analysis | simulation | random number generator | counting | modeling uncertainties | random variables | statistical package | flexibility | contingency rules | excel solver | solver

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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15.066J System Optimization and Analysis for Manufacturing (MIT)

Description

One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management.

Subjects

modeling | optimization | simulation | manufacturing systems | decision making | decision support | probabilistic simulation | designing manufacturing systems | operations management | linear programming | sensitivity analysis | network flow problems | non-linear programming | Lagrange multipliers | integer programming | discrete-event simulation | heuristics | algorithms | 15.066 | 2.851 | 3.83

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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ESD.70J Engineering Economy Module (MIT)

Description

This intensive micro-subject provides the necessary skills in Microsoft® Excel spreadsheet modeling for ESD.71 Engineering Systems Analysis for Design. Its purpose is to bring entering students up to speed on some of the advanced techniques that we routinely use in analysis. It is motivated by our experience that many students only have an introductory knowledge of Excel, and thus waste a lot of time thrashing about unproductively. Many people think they know Excel, but overlook many efficient tools, such as Data Table and Goal Seek. It is also useful for a variety of other subjects.

Subjects

ESD.70 | 1.145 | excel | spreadsheet | modeling | dynamic modeling | analysis | data table | goal seek | sensitivity analysis | simulation | random number generator | counting | modeling uncertainties | random variables | statistical package | flexibility | contingency rules | excel solver | solver

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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HST.750 Modeling Issues in Speech and Hearing (MIT)

Description

This course explores the theory and practice of scientific modeling in the context of auditory and speech biophysics. Based on seminar-style discussions of the research literature, the class draws on examples from hearing and speech, and explores general, meta-theoretical issues that transcend the particular subject matter. Examples include: What is a model? What is the process of model building? What are the different approaches to modeling? What is the relationship between theory and experiment? How are models tested? What constitutes a good model?

Subjects

hearing | speech | modeling biology | network model of the ear | model building | dimensional analysis and scaling | resampling | monte carlo | forward vs. inverse | chaos | limits of prediction | hodgkin | huxley | molecular mathematic biology | cochlear input impedance | auditory network | auditory morphology | electric model of neural cell fiber | electric diagrams of neural cells | linear regression | sensitivity analysis | cochlea | inner ear | middle ear | auditory cortex | scientific literature | analysis | paper analysis | tent maps | quadratic maps

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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16.90 Computational Methods in Aerospace Engineering (MIT)

Description

This course provides an introduction to numerical methods and computational techniques arising in aerospace engineering. Applications are drawn from aerospace structures, aerodynamics, dynamics and control, and aerospace systems. Techniques covered include numerical integration of systems of ordinary differential equations; numerical discretization of partial differential equations; and probabilistic methods for quantifying the impact of variability. Specific emphasis is given to finite volume methods in fluid mechanics, and finite element methods in structural mechanics.Acknowledgement: Prof. David Darmofal taught this course in prior years, and created some of the materials found in this OCW site.

Subjects

numerical integration | ODEs | ordinary differential equations | finite difference | finite volume | finite element | discretization | PDEs | partial differential equations | numerical linear algebra | probabilistic methods | optimization | computational methods | aerospace engineering | Monte Carlo | Fourier stability analysis | Matrix stability analysis | Runge-Kutta | convergence | accuracy | stiffness | weighted residual | statistical sampling | sensitivity analysis

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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