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15.082J Network Optimization (MIT) 15.082J Network Optimization (MIT)

Description

15.082J/6.855J is an H-level graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, finance as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. 15.082J/6.855J is an H-level graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, finance as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem.Subjects

network flows | network flows | extensions | extensions | network flow problems | network flow problems | transportation | transportation | logistics | logistics | manufacturing | manufacturing | computer science | computer science | project management | project management | finance | finance | the shortest path problem | the shortest path problem | the maximum flow problem | the maximum flow problem | the minimum cost flow problem | the minimum cost flow problem | the multi-commodity flow problem | the multi-commodity flow problem | communication | communication | systems | systems | applications | applications | efficiency | efficiency | algorithms | algorithms | traffic | traffic | equilibrium | equilibrium | design | design | mplementation | mplementation | linear programming | linear programming | implementation | implementation | computer | computer | science | science | linear | linear | programming | programming | network | network | flow | flow | problems | problems | project | project | management | management | maximum | maximum | minimum | minimum | cost | cost | multi-commodity | multi-commodity | shortest | shortest | path | path | 15.082 | 15.082 | 6.855 | 6.855License

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See all metadata15.082J Network Optimization (MIT) 15.082J Network Optimization (MIT)

Description

15.082J/6.855J/ESD.78J is a graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, and finance, as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. We will also consider other extensions of network flow problems. 15.082J/6.855J/ESD.78J is a graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, and finance, as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. We will also consider other extensions of network flow problems.Subjects

15.082 | 15.082 | 6.855 | 6.855 | ESD.78 | ESD.78 | network models | network models | network design | network design | maximum flow algorithm | maximum flow algorithm | minimum cost flow | minimum cost flow | shortest path algorithm | shortest path algorithm | algorithm efficiency | algorithm efficiency | preflow push algorithm | preflow push algorithm | data structures | data structuresLicense

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.htmSite sourced from

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See all metadata15.082J Network Optimization (MIT)

Description

15.082J/6.855J is an H-level graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, finance as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem.Subjects

network flows | extensions | network flow problems | transportation | logistics | manufacturing | computer science | project management | finance | the shortest path problem | the maximum flow problem | the minimum cost flow problem | the multi-commodity flow problem | communication | systems | applications | efficiency | algorithms | traffic | equilibrium | design | mplementation | linear programming | implementation | computer | science | linear | programming | network | flow | problems | project | management | maximum | minimum | cost | multi-commodity | shortest | path | 15.082 | 6.855License

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.htmSite sourced from

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See all metadataálisis de problemas y casos en Psicología (2010) álisis de problemas y casos en Psicología (2010)

Description

El proceso de construcción del Espacio Europeo de Educación Superior (EEES) tiene entre otros, como objetivo, el desarrollo de metodologías basadas en el aprendizaje del estudiante. En este tipo de metodologías, el alumno pasa a ser el auténtico eje de la educación universitaria y el profesor un mediador o guía de dicho proceso de aprendizaje. Esta asignatura se inserta en el contexto de una de estas metodologías, a saber, la del aprendizaje cooperativo y basado en problemas. El aprendizaje basado en problemas (a partir de ahora, ABP) es una estrategia de aprendizaje grupal mediante el cual el alumno construye su propio aprendizaje a través de problemas de la vida real, problemas abiertos, y no siempre con una solución única, tal y como suele ocurrir en el ámbito profesional. El proceso de construcción del Espacio Europeo de Educación Superior (EEES) tiene entre otros, como objetivo, el desarrollo de metodologías basadas en el aprendizaje del estudiante. En este tipo de metodologías, el alumno pasa a ser el auténtico eje de la educación universitaria y el profesor un mediador o guía de dicho proceso de aprendizaje. Esta asignatura se inserta en el contexto de una de estas metodologías, a saber, la del aprendizaje cooperativo y basado en problemas. El aprendizaje basado en problemas (a partir de ahora, ABP) es una estrategia de aprendizaje grupal mediante el cual el alumno construye su propio aprendizaje a través de problemas de la vida real, problemas abiertos, y no siempre con una solución única, tal y como suele ocurrir en el ámbito profesional.License

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See all metadata16.346 Astrodynamics (MIT) 16.346 Astrodynamics (MIT)

Description

Includes audio/video content: AV selected lectures. This course covers the fundamentals of astrodynamics, focusing on the two-body orbital initial-value and boundary-value problems with applications to space vehicle navigation and guidance for lunar and planetary missions, including both powered flight and midcourse maneuvers. Other topics include celestial mechanics, Kepler's problem, Lambert's problem, orbit determination, multi-body methods, mission planning, and recursive algorithms for space navigation. Selected applications from the Apollo, Space Shuttle, and Mars exploration programs are also discussed. Includes audio/video content: AV selected lectures. This course covers the fundamentals of astrodynamics, focusing on the two-body orbital initial-value and boundary-value problems with applications to space vehicle navigation and guidance for lunar and planetary missions, including both powered flight and midcourse maneuvers. Other topics include celestial mechanics, Kepler's problem, Lambert's problem, orbit determination, multi-body methods, mission planning, and recursive algorithms for space navigation. Selected applications from the Apollo, Space Shuttle, and Mars exploration programs are also discussed.Subjects

space navigation | space navigation | two body problem | two body problem | boundary value problem | boundary value problem | Kepler | Kepler | astrodynamics | astrodynamics | orbital transfer | orbital transfer | satellite | satellite | hyperbolic orbits | hyperbolic orbits | planetary flybys | planetary flybys | hypergeometric functions | hypergeometric functions | flight guidance | flight guidance | three body problem | three body problem | Clohessy-Wiltshire equation | Clohessy-Wiltshire equation | Hodograph plane | Hodograph plane | Battin-vaughan formulation | Battin-vaughan formulation | atmospheric drag | atmospheric drag | disturbing function | disturbing functionLicense

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.htmSite sourced from

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See all metadata18.335J Introduction to Numerical Methods (MIT) 18.335J Introduction to Numerical Methods (MIT)

Description

The focus of this course is on numerical linear algebra and numerical methods for solving ordinary differential equations. Topics include linear systems of equations, least square problems, eigenvalue problems, and singular value problems. The focus of this course is on numerical linear algebra and numerical methods for solving ordinary differential equations. Topics include linear systems of equations, least square problems, eigenvalue problems, and singular value problems.Subjects

linear algebra | linear algebra | ordinary differential equations | ordinary differential equations | linear systems of equations | linear systems of equations | least square problems | least square problems | eigenvalue problems | eigenvalue problems | singular value problems | singular value problemsLicense

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.htmSite sourced from

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We will discuss numerous research problems that are related to the internet. Sample topics include: routing algorithms such as BGP, communication protocols such as TCP, algorithms for intelligently selecting a resource in the face of uncertainty, bandwidth sensing tools, load balancing algorithms, streaming protocols, determining the structure of the internet, cost optimization, DNS-related problems, visualization, and large-scale data processing. The seminar is intended for students who are ready to work on challenging research problems. Each lecture will discuss: methods used today issues and problems formulation of concrete problems potential new lines of research A modest amount of background information will be provided so that the importance and context of the problems can be under We will discuss numerous research problems that are related to the internet. Sample topics include: routing algorithms such as BGP, communication protocols such as TCP, algorithms for intelligently selecting a resource in the face of uncertainty, bandwidth sensing tools, load balancing algorithms, streaming protocols, determining the structure of the internet, cost optimization, DNS-related problems, visualization, and large-scale data processing. The seminar is intended for students who are ready to work on challenging research problems. Each lecture will discuss: methods used today issues and problems formulation of concrete problems potential new lines of research A modest amount of background information will be provided so that the importance and context of the problems can be underLicense

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.htmSite sourced from

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See all metadataEngineering: The nature of problems Engineering: The nature of problems

Description

Engineering is about extending the horizons of society by solving technical problems, ranging from the meeting of basic human needs for food and shelter to the generation of wealth by trade. In this free course, Engineering: The nature of problems, we learn that engineers see the problems more as challenges and opportunities than as difficulties. What they appear to be doing is solving problems, but in fact they are busy creating solutions, an altogether more imaginative activity. First published on Wed, 23 Mar 2016 as Engineering: The nature of problems. To find out more visit The Open University's Openlearn website. Creative-Commons 2016 Engineering is about extending the horizons of society by solving technical problems, ranging from the meeting of basic human needs for food and shelter to the generation of wealth by trade. In this free course, Engineering: The nature of problems, we learn that engineers see the problems more as challenges and opportunities than as difficulties. What they appear to be doing is solving problems, but in fact they are busy creating solutions, an altogether more imaginative activity. First published on Wed, 23 Mar 2016 as Engineering: The nature of problems. To find out more visit The Open University's Openlearn website. Creative-Commons 2016License

Except for third party materials and otherwise stated (see http://www.open.ac.uk/conditions terms and conditions), this content is made available under a http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Licence Licensed under a Creative Commons Attribution - NonCommercial-ShareAlike 2.0 Licence - see http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ - Original copyright The Open UniversitySite sourced from

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See all metadataón a la Filosofía ón a la Filosofía

Description

La asignatura plantea de manera sistemática algunas de las cuestiones que recorren la historia de la filosofía, como problemas tradicionales, que, no obstante, alcanzan nuestro presente. Por tanto, se puede considerar una asignatura básica para comenzar a comprender los fundamentos de la epistemología, le metafísica, la ética, la política o la antropología filosófica. Sin embargo, el abordaje de estas cuestiones abstractas se hará partiendo de la que puede considerarse la figura fundacional de la filosofía: Sócrates. constituye un foco de interés en el contexto de un life-long training, tal como se manifiesta en la permanente demanda de este tipo de estudios en los cursos de educación permanente, en todos los niveles. En tercer lugar, enseñar a leer filosofía, a buscar las La asignatura plantea de manera sistemática algunas de las cuestiones que recorren la historia de la filosofía, como problemas tradicionales, que, no obstante, alcanzan nuestro presente. Por tanto, se puede considerar una asignatura básica para comenzar a comprender los fundamentos de la epistemología, le metafísica, la ética, la política o la antropología filosófica. Sin embargo, el abordaje de estas cuestiones abstractas se hará partiendo de la que puede considerarse la figura fundacional de la filosofía: Sócrates. constituye un foco de interés en el contexto de un life-long training, tal como se manifiesta en la permanente demanda de este tipo de estudios en los cursos de educación permanente, en todos los niveles. En tercer lugar, enseñar a leer filosofía, a buscar lasSubjects

El problema del escepticismo | El problema del escepticismo | El conocimiento y el amor | El conocimiento y el amor | Humanidades | Humanidades | El problema del bien | El problema del bien | El problema mente/cuerpo | El problema mente/cuerpo | ócrates | ócrates | ía | ía | El problema del conocimiento | El problema del conocimiento | La vida examinada | La vida examinada | ófico | ófico | 2012 | 2012 | Filosofia | FilosofiaLicense

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This course analyzes initial and boundary value problems for ordinary differential equations and the wave and heat equation in one space dimension. It also covers the Sturm-Liouville theory and eigenfunction expansions, as well as the Dirichlet problem for Laplace's operator and potential theory. This course analyzes initial and boundary value problems for ordinary differential equations and the wave and heat equation in one space dimension. It also covers the Sturm-Liouville theory and eigenfunction expansions, as well as the Dirichlet problem for Laplace's operator and potential theory.Subjects

Initial and boundary value problems for ordinary differential equations | Initial and boundary value problems for ordinary differential equations | Sturm-Liouville theory and eigenfunction expansions | Sturm-Liouville theory and eigenfunction expansions | Initial value problems for the wave equation and heat equation | Initial value problems for the wave equation and heat equation | The Dirichlet problem for Laplace's operator and potential theory | The Dirichlet problem for Laplace's operator and potential theoryLicense

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.htmSite sourced from

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This course offers an introduction to optimization problems, algorithms, and their complexity, emphasizing basic methodologies and the underlying mathematical structures. The main topics covered include: Theory and algorithms for linear programming Network flow problems and algorithms Introduction to integer programming and combinatorial problems This course offers an introduction to optimization problems, algorithms, and their complexity, emphasizing basic methodologies and the underlying mathematical structures. The main topics covered include: Theory and algorithms for linear programming Network flow problems and algorithms Introduction to integer programming and combinatorial problemsSubjects

optimization | optimization | algorithms | algorithms | linear programming | linear programming | network flow problems | network flow problems | integer programming | integer programming | combinatorial problems | combinatorial problems | mathematics | mathematics | mathematical programming | mathematical programming | 6.251 | 6.251 | 15.081 | 15.081License

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.htmSite sourced from

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See all metadata6.034 Artificial Intelligence (MIT) 6.034 Artificial Intelligence (MIT)

Description

Includes audio/video content: AV lectures. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective. Includes audio/video content: AV lectures. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Subjects

artificial intelligence | artificial intelligence | knowledge representation | knowledge representation | problem solving | problem solving | learning methods | learning methods | intelligent systems | intelligent systems | basic search | basic search | optimal search | optimal search | neural nets | neural nets | genetic algorithms | genetic algorithms | support vector machines | support vector machines | boosting | boosting | probabilistic inference | probabilistic inferenceLicense

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.htmSite sourced from

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This course surveys operations research models and techniques developed for a variety of problems arising in logistical planning of multi-echelon systems. There is a focus on planning models for production/inventory/distribution strategies in general multi-echelon multi-item systems. Topics include vehicle routing problems, dynamic lot sizing inventory models, stochastic and deterministic multi-echelon inventory systems, the bullwhip effect, pricing models, and integration problems arising in supply chain management. Probability and linear programming experience required. This course surveys operations research models and techniques developed for a variety of problems arising in logistical planning of multi-echelon systems. There is a focus on planning models for production/inventory/distribution strategies in general multi-echelon multi-item systems. Topics include vehicle routing problems, dynamic lot sizing inventory models, stochastic and deterministic multi-echelon inventory systems, the bullwhip effect, pricing models, and integration problems arising in supply chain management. Probability and linear programming experience required.Subjects

ESD.273 | ESD.273 | 1.270 | 1.270 | vehicle routing problems | vehicle routing problems | dynamic lot sizing inventory models | dynamic lot sizing inventory models | stochastic and deterministic multi-echelon inventory systems | stochastic and deterministic multi-echelon inventory systems | the bullwhip effect | the bullwhip effect | pricing models | pricing models | integration problems | integration problemsLicense

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.htmSite sourced from

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See all metadataSocial problems: Who makes them? Social problems: Who makes them?

Description

Anti-social behaviour, homelessness, drugs, mental illness: all problems in today's society. But what makes a problem social? This free course, Social problems: Who makes them?, will help you to discover how these issues are identified, defined, given meaning and acted upon. You will also look at the conflicts within social science in this area. First published on Mon, 07 Mar 2016 as Social problems: Who makes them?. To find out more visit The Open University's Openlearn website. Creative-Commons 2016 Anti-social behaviour, homelessness, drugs, mental illness: all problems in today's society. But what makes a problem social? This free course, Social problems: Who makes them?, will help you to discover how these issues are identified, defined, given meaning and acted upon. You will also look at the conflicts within social science in this area. First published on Mon, 07 Mar 2016 as Social problems: Who makes them?. To find out more visit The Open University's Openlearn website. Creative-Commons 2016License

Except for third party materials and otherwise stated (see http://www.open.ac.uk/conditions terms and conditions), this content is made available under a http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Licence Licensed under a Creative Commons Attribution - NonCommercial-ShareAlike 2.0 Licence - see http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ - Original copyright The Open UniversitySite sourced from

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See all metadata6.034 Artificial Intelligence (MIT) 6.034 Artificial Intelligence (MIT)

Description

6.034 is the header course for the department's "Artificial Intelligence and Applications" concentration. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective. 6.034 is the header course for the department's "Artificial Intelligence and Applications" concentration. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Subjects

artificial intelligence | artificial intelligence | applied systems | applied systems | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systemsLicense

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.htmSite sourced from

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See all metadata2.23 Hydrofoils and Propellers (13.04) (MIT) 2.23 Hydrofoils and Propellers (13.04) (MIT)

Description

This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic win This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic winSubjects

Theory and design of hydrofoil sections | Theory and design of hydrofoil sections | lifting and thickness problems | lifting and thickness problems | sub-cavitating sections | sub-cavitating sections | unsteady flow problems | unsteady flow problems | computer-aided design | computer-aided design | low drag | low drag | cavitation free sections | cavitation free sections | Lifting line and lifting surface theory | Lifting line and lifting surface theory | hydrofoil craft | hydrofoil craft | rudder | rudder | and control surface design | and control surface design | propeller lifting line | propeller lifting line | lifting surface theory | lifting surface theory | wake adapted propellers | wake adapted propellers | unsteady propeller thrust and torque | unsteady propeller thrust and torque | axially symmetric bodies | axially symmetric bodies | low-aspect ratio lifting surfaces | low-aspect ratio lifting surfaces | Hydrodynamic performance | Hydrodynamic performance | design of waterjets | design of waterjets | wind turbine rotors in steady and stochastic wind | wind turbine rotors in steady and stochastic wind | hydrofoil craft | rudder | and control surface design | hydrofoil craft | rudder | and control surface design | 9. low drag | cavitation free sections | 9. low drag | cavitation free sections | 5. hydrofoil craft | rudder | and control surface design | 5. hydrofoil craft | rudder | and control surface design | low drag | cavitation free sections | low drag | cavitation free sectionsLicense

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.htmSite sourced from

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See all metadata6.034 Artificial Intelligence (MIT) 6.034 Artificial Intelligence (MIT)

Description

This course introduces students to the basic knowledge representation, problem solving, and learning methods of  artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Technical RequirementsJava® plug-in software is required to run the .jar files found on this course site.Java® is a trademark or registered trademark of Sun Microsystems, Inc. in the United States and other countries. This course introduces students to the basic knowledge representation, problem solving, and learning methods of  artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Technical RequirementsJava® plug-in software is required to run the .jar files found on this course site.Java® is a trademark or registered trademark of Sun Microsystems, Inc. in the United States and other countries.Subjects

artificial intelligence | artificial intelligence | applied systems | applied systems | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systemsLicense

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.htmSite sourced from

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See all metadata18.404J Theory of Computation (MIT) 18.404J Theory of Computation (MIT)

Description

A more extensive and theoretical treatment of the material in 18.400J, Automata, Computability, and Complexity, 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. A more extensive and theoretical treatment of the material in 18.400J, Automata, Computability, and Complexity, 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.Subjects

computability | computability | computational complexity theory | computational complexity theory | Regular and context-free languages | Regular and context-free languages | Decidable and undecidable problems | Decidable and undecidable problems | reducibility | reducibility | recursive function theory | recursive function theory | Time and space measures on computation | Time and space measures on computation | completeness | completeness | hierarchy theorems | hierarchy theorems | inherently complex problems | inherently complex problems | oracles | oracles | probabilistic computation | probabilistic computation | interactive proof systems | interactive proof systems | 18.404 | 18.404 | 6.840 | 6.840License

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See all metadata13.04 Hydrofoils and Propellers (MIT) 13.04 Hydrofoils and Propellers (MIT)

Description

This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic win This course deals with theory and design of hydrofoil sections; lifting and thickness problems for sub-cavitating sections, unsteady flow problems. It focuses on computer-aided design of low drag, cavitation free sections. The course also covers lifting line and lifting surface theory with applications to hydrofoil craft, rudder, and control surface design. Topics include propeller lifting line and lifting surface theory; computer-aided design of wake adapted propellers, unsteady propeller thrust and torque. The course is also an introduction to subjects like flow about axially symmetric bodies and low-aspect ratio lifting surfaces, and hydrodynamic performance and design of waterjets. We will also do an analysis of performance and design of wind turbine rotors in steady and stochastic winSubjects

Theory and design of hydrofoil sections | Theory and design of hydrofoil sections | lifting and thickness problems | lifting and thickness problems | sub-cavitating sections | sub-cavitating sections | unsteady flow problems | unsteady flow problems | computer-aided design | computer-aided design | low drag | low drag | cavitation free sections | cavitation free sections | Lifting line and lifting surface theory | Lifting line and lifting surface theory | hydrofoil craft | hydrofoil craft | rudder | rudder | and control surface design | and control surface design | propeller lifting line | propeller lifting line | lifting surface theory | lifting surface theory | wake adapted propellers | wake adapted propellers | unsteady propeller thrust and torque | unsteady propeller thrust and torque | axially symmetric bodies | axially symmetric bodies | low-aspect ratio lifting surfaces | low-aspect ratio lifting surfaces | Hydrodynamic performance | Hydrodynamic performance | design of waterjets | design of waterjets | wind turbine rotors in steady and stochastic wind | wind turbine rotors in steady and stochastic wind | hydrofoil craft | rudder | and control surface design | hydrofoil craft | rudder | and control surface design | 9. low drag | cavitation free sections | 9. low drag | cavitation free sections | 5. hydrofoil craft | rudder | and control surface design | 5. hydrofoil craft | rudder | and control surface design | low drag | cavitation free sections | low drag | cavitation free sections | 2.23 | 2.23License

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El aprendizaje basado en problemas (a partir de ahora, ABP) es una estrategia de aprendizaje grupal mediante el cual el alumno construye su propio aprendizaje a través de problemas de la vida real, problemas abiertos, y no siempre con una solución única, tal y como suele ocurrir en el ámbito profesional. No se trata simplemente de que el alumno aplique conocimientos o de que encuentre la solución acertada en cada caso, sino de que sea capaz de construir su propio conocimiento. El proceso de aprendizaje comienza por la presentación de un determinado problema preparado por el equipo docente de la asignatura. Una vez conformados los grupos de trabajo se entra en una fase de discusión y debate en el seno de cada grupo, tutorizado por un profesor, con el fin de analizar los distintos ele El aprendizaje basado en problemas (a partir de ahora, ABP) es una estrategia de aprendizaje grupal mediante el cual el alumno construye su propio aprendizaje a través de problemas de la vida real, problemas abiertos, y no siempre con una solución única, tal y como suele ocurrir en el ámbito profesional. No se trata simplemente de que el alumno aplique conocimientos o de que encuentre la solución acertada en cada caso, sino de que sea capaz de construir su propio conocimiento. El proceso de aprendizaje comienza por la presentación de un determinado problema preparado por el equipo docente de la asignatura. Una vez conformados los grupos de trabajo se entra en una fase de discusión y debate en el seno de cada grupo, tutorizado por un profesor, con el fin de analizar los distintos eleSubjects

Aprendizaje Cooperativo | Aprendizaje Cooperativo | Aprendizaje Basado en Problemas | Aprendizaje Basado en Problemas | ía | ía | ón educativa | ón educativa | Competencias Trasversales | Competencias TrasversalesLicense

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See all metadata1.206J Airline Schedule Planning (MIT) 1.206J Airline Schedule Planning (MIT)

Description

Explores a variety of models and optimization techniques for the solution of airline schedule planning and operations problems. Schedule design, fleet assignment, aircraft maintenance routing, crew scheduling, passenger mix, and other topics are covered. Recent models and algorithms addressing issues of model integration, robustness, and operations recovery are introduced. Modeling and solution techniques designed specifically for large-scale problems, and state-of-the-art applications of these techniques to airline problems are detailed. Explores a variety of models and optimization techniques for the solution of airline schedule planning and operations problems. Schedule design, fleet assignment, aircraft maintenance routing, crew scheduling, passenger mix, and other topics are covered. Recent models and algorithms addressing issues of model integration, robustness, and operations recovery are introduced. Modeling and solution techniques designed specifically for large-scale problems, and state-of-the-art applications of these techniques to airline problems are detailed.Subjects

Airline Schedule Planning | Airline Schedule Planning | Optimization | Optimization | Operations | Operations | Fleet Assignment | Fleet Assignment | Aircraft Maintenance Routing | Aircraft Maintenance Routing | Crew Scheduling | Crew Scheduling | Passenger Mix | Passenger Mix | Model Integration | Model Integration | Robustness | Robustness | Operations Recovery | Operations Recovery | models | models | optimization techniques | optimization techniques | airline schedule planning problems | airline schedule planning problems | schedule design | schedule design | fleet assignment | fleet assignment | aircraft maintenance routing | aircraft maintenance routing | crew scheduling | crew scheduling | robust planning | robust planning | passenger mix | passenger mix | integrated schedule planning | integrated schedule planning | solution techniques | solution techniques | decomposition | decomposition | Lagrangian relaxation | Lagrangian relaxation | column generation | column generation | partitioning | partitioning | applications | applications | algorithms | algorithms | model integration | model integration | robustness | robustness | operations recovery | operations recovery | airline schedule planning | airline schedule planning | 16.77 | 16.77 | ESD.215 | ESD.215License

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This course is an introduction to linear optimization and its extensions emphasizing the underlying mathematical structures, geometrical ideas, algorithms and solutions of practical problems. The topics covered include: formulations, the geometry of linear optimization, duality theory, the simplex method, sensitivity analysis, robust optimization, large scale optimization network flows, solving problems with an exponential number of constraints and the ellipsoid method, interior point methods, semidefinite optimization, solving real world problems problems with computer software, discrete optimization formulations and algorithms. This course is an introduction to linear optimization and its extensions emphasizing the underlying mathematical structures, geometrical ideas, algorithms and solutions of practical problems. The topics covered include: formulations, the geometry of linear optimization, duality theory, the simplex method, sensitivity analysis, robust optimization, large scale optimization network flows, solving problems with an exponential number of constraints and the ellipsoid method, interior point methods, semidefinite optimization, solving real world problems problems with computer software, discrete optimization formulations and algorithms.Subjects

Formulations | Formulations | Simplex method | Simplex method | Duality theory | Duality theory | Sensitivity analysis | Sensitivity analysis | Robust optimization | Robust optimization | Large scale optimization | Large scale optimization | Network flows | Network flows | The Ellipsoid method | The Ellipsoid method | Interior point methods | Interior point methods | Semidefinite optimization | Semidefinite optimization | Discrete optimization | Discrete optimizationLicense

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6.336J is an introduction to computational techniques for the simulation of a large variety of engineering and physical systems. Applications are drawn from aerospace, mechanical, electrical, chemical and biological engineering, and materials science. Topics include: mathematical formulations; network problems; sparse direct and iterative matrix solution techniques; Newton methods for nonlinear problems; discretization methods for ordinary, time-periodic and partial differential equations, fast methods for partial differential and integral equations, techniques for dynamical system model reduction and approaches for molecular dynamics. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5211 (Introduction to Numerical Simulation). 6.336J is an introduction to computational techniques for the simulation of a large variety of engineering and physical systems. Applications are drawn from aerospace, mechanical, electrical, chemical and biological engineering, and materials science. Topics include: mathematical formulations; network problems; sparse direct and iterative matrix solution techniques; Newton methods for nonlinear problems; discretization methods for ordinary, time-periodic and partial differential equations, fast methods for partial differential and integral equations, techniques for dynamical system model reduction and approaches for molecular dynamics. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5211 (Introduction to Numerical Simulation).Subjects

Numerical Simulation | Numerical Simulation | simulation | simulation | mathematics | mathematics | network problems | network problems | matrix solution | matrix solution | Newton method | Newton method | nonlinear problems | nonlinear problems | discretization methods | discretization methods | differential equations | differential equations | integral equations | integral equations | model-order reduction | model-order reduction | Monte Carlo | Monte Carlo | 6.336 | 6.336 | 2.096 | 2.096 | 16.910 | 16.910License

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This course provides students with the basic analytical and computational tools of linear partial differential equations (PDEs) for practical applications in science engineering, including heat/diffusion, wave, and Poisson equations. Analytics emphasize the viewpoint of linear algebra and the analogy with finite matrix problems. Numerics focus on finite-difference and finite-element techniques to reduce PDEs to matrix problems. This course provides students with the basic analytical and computational tools of linear partial differential equations (PDEs) for practical applications in science engineering, including heat/diffusion, wave, and Poisson equations. Analytics emphasize the viewpoint of linear algebra and the analogy with finite matrix problems. Numerics focus on finite-difference and finite-element techniques to reduce PDEs to matrix problems.Subjects

diffusion | diffusion | Laplace equations | Laplace equations | Poisson | Poisson | wave equations | wave equations | separation of variables | separation of variables | Fourier series | Fourier series | Fourier transforms | Fourier transforms | eigenvalue problems | eigenvalue problems | Green's function | Green's function | Heat Equation | Heat Equation | Sturm-Liouville Eigenvalue problems | Sturm-Liouville Eigenvalue problems | quasilinear PDEs | quasilinear PDEs | Bessel functionsORDS | Bessel functionsORDSLicense

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.htmSite sourced from

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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. 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.Subjects

advection equation | advection equation | heat equation | heat equation | wave equation | wave equation | Airy equation | Airy equation | convection-diffusion problems | convection-diffusion problems | KdV equation | KdV equation | hyperbolic conservation laws | hyperbolic conservation laws | Poisson equation | Poisson equation | Stokes problem | Stokes problem | Navier-Stokes equations | Navier-Stokes equations | interface problems | interface problems | consistency | consistency | stability | stability | convergence | convergence | Lax equivalence theorem | Lax equivalence theorem | error analysis | error analysis | Fourier approaches | Fourier approaches | staggered grids | staggered grids | shocks | shocks | front propagation | front propagation | preconditioning | preconditioning | multigrid | multigrid | Krylov spaces | Krylov spaces | saddle point problems | saddle point problems | finite differences | finite differences | finite volumes | finite volumes | finite elements | finite elements | ENO/WENO | ENO/WENO | spectral methods | spectral methods | projection approaches for incompressible ows | projection approaches for incompressible ows | level set methods | level set methods | particle methods | particle methods | direct and iterative methods | direct and iterative methodsLicense

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.htmSite sourced from

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