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Description

Numerical methods for solving problems arising in heat and mass transfer, fluid mechanics, chemical reaction engineering, and molecular simulation. Topics: numerical linear algebra, solution of nonlinear algebraic equations and ordinary differential equations, solution of partial differential equations (e.g. Navier-Stokes), numerical methods in molecular simulation (dynamics, geometry optimization). All methods are presented within the context of chemical engineering problems. Familiarity with structured programming is assumed. The examples will use MATLAB®. Acknowledgements The instructor would like to thank Robert Ashcraft, Sandeep Sharma, David Weingeist, and Nikolay Zaborenko for their work in preparing materials for this course site. Numerical methods for solving problems arising in heat and mass transfer, fluid mechanics, chemical reaction engineering, and molecular simulation. Topics: numerical linear algebra, solution of nonlinear algebraic equations and ordinary differential equations, solution of partial differential equations (e.g. Navier-Stokes), numerical methods in molecular simulation (dynamics, geometry optimization). All methods are presented within the context of chemical engineering problems. Familiarity with structured programming is assumed. The examples will use MATLAB®. Acknowledgements The instructor would like to thank Robert Ashcraft, Sandeep Sharma, David Weingeist, and Nikolay Zaborenko for their work in preparing materials for this course site.Subjects

Matlab | Matlab | modern computational techniques in chemical engineering | modern computational techniques in chemical engineering | mathematical techniques in chemical engineering | mathematical techniques in chemical engineering | linear systems | linear systems | scientific computing | scientific computing | solving sets of nonlinear algebraic equations | solving sets of nonlinear algebraic equations | solving ordinary differential equations | solving ordinary differential equations | solving differential-algebraic (DAE) systems | solving differential-algebraic (DAE) systems | probability theory | probability theory | use of probability theory in physical modeling | use of probability theory in physical modeling | statistical analysis of data estimation | statistical analysis of data estimation | statistical analysis of parameter estimation | statistical analysis of parameter estimation | finite difference techniques | finite difference techniques | finite element techniques | finite element techniques | converting partial differential equations | converting partial differential equations | Navier-Stokes equations | Navier-Stokes equationsLicense

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 focuses on the use of modern computational and mathematical techniques in chemical engineering. Starting from a discussion of linear systems as the basic computational unit in scientific computing, methods for solving sets of nonlinear algebraic equations, ordinary differential equations, and differential-algebraic (DAE) systems are presented. Probability theory and its use in physical modeling is covered, as is the statistical analysis of data and parameter estimation. The finite difference and finite element techniques are presented for converting the partial differential equations obtained from transport phenomena to DAE systems. The use of these techniques will be demonstrated throughout the course in the MATLAB® computing environment. This course focuses on the use of modern computational and mathematical techniques in chemical engineering. Starting from a discussion of linear systems as the basic computational unit in scientific computing, methods for solving sets of nonlinear algebraic equations, ordinary differential equations, and differential-algebraic (DAE) systems are presented. Probability theory and its use in physical modeling is covered, as is the statistical analysis of data and parameter estimation. The finite difference and finite element techniques are presented for converting the partial differential equations obtained from transport phenomena to DAE systems. The use of these techniques will be demonstrated throughout the course in the MATLAB® computing environment.Subjects

Matlab | Matlab | modern computational techniques in chemical engineering | modern computational techniques in chemical engineering | mathematical techniques in chemical engineering | mathematical techniques in chemical engineering | linear systems | linear systems | scientific computing | scientific computing | solving sets of nonlinear algebraic equations | solving sets of nonlinear algebraic equations | solving ordinary differential equations | solving ordinary differential equations | solving differential-algebraic (DAE) systems | solving differential-algebraic (DAE) systems | probability theory | probability theory | use of probability theory in physical modeling | use of probability theory in physical modeling | statistical analysis of data estimation | statistical analysis of data estimation | statistical analysis of parameter estimation | statistical analysis of parameter estimation | finite difference techniques | finite difference techniques | finite element techniques | finite element techniques | converting partial differential equations | converting partial differential equationsLicense

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 metadata6.856J Randomized Algorithms (MIT) 6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms. This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.Subjects

Randomized Algorithms | Randomized Algorithms | algorithms | algorithms | efficient in time and space | efficient in time and space | randomization | randomization | computational problems | computational problems | data structures | data structures | graph algorithms | graph algorithms | optimization | optimization | geometry | geometry | Markov chains | Markov chains | sampling | sampling | estimation | estimation | geometric algorithms | geometric algorithms | parallel and distributed algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and ditributed algorithm | parallel and distributed algorithm | parallel and distributed algorithm | random sampling | random sampling | random selection of witnesses | random selection of witnesses | symmetry breaking | symmetry breaking | randomized computational models | randomized computational models | hash tables | hash tables | skip lists | skip lists | minimum spanning trees | minimum spanning trees | shortest paths | shortest paths | minimum cuts | minimum cuts | convex hulls | convex hulls | linear programming | linear programming | fixed dimension | fixed dimension | arbitrary dimension | arbitrary dimension | approximate counting | approximate counting | parallel algorithms | parallel algorithms | online algorithms | online algorithms | derandomization techniques | derandomization techniques | probabilistic analysis | probabilistic analysis | computational number theory | computational number theory | simplicity | simplicity | speed | speed | design | design | basic probability theory | basic probability theory | application | application | randomized complexity classes | randomized complexity classes | game-theoretic techniques | game-theoretic techniques | Chebyshev | Chebyshev | moment inequalities | moment inequalities | limited independence | limited independence | coupon collection | coupon collection | occupancy problems | occupancy problems | tail inequalities | tail inequalities | Chernoff bound | Chernoff bound | conditional expectation | conditional expectation | probabilistic method | probabilistic method | random walks | random walks | algebraic techniques | algebraic techniques | probability amplification | probability amplification | sorting | sorting | searching | searching | combinatorial optimization | combinatorial optimization | approximation | approximation | counting problems | counting problems | distributed algorithms | distributed algorithms | 6.856 | 6.856 | 18.416 | 18.416License

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 introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.Subjects

programming languages | programming languages | techniques used by physical scientists | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++; Matlab | C++; Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification. | methods of dissemination and verification. | C++ | C++ | Matlab | Matlab | programming languages | techniques used by physical scientists | programming languages | techniques used by physical scientistsLicense

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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Wales undergraduate level and as a CPD training resourceSubjects

ukoer | sfsoer | oer | open educational resources | metadata | analytical science | cpd training resource | analytical chemistry | measurement science | analytical process model | skills for analytical science | skills for analytical chemistry | analytical sample preparation | separation and concentration of analytes | units of measurement | volumetric techniques | gravimetric techniques | calibration methods | standard-addition | method of internal-standards | statistical analysis of data | measurement uncertainty | chromatographic methods | thin layer chromatography | gc | gas chromatography | hplc | high-performance liquid chromatography | capillary electrophoresis | potentiometry | ion-selective electrodes | amperometry | coulometry | plated film thickness | electromagnetic spectrum | electronic transitions | vibrational energy | comparison of spectroscopic techniques | fluorescence spectroscopy | mid infra-red spectroscopy | near infra-red spectroscopy | aas | atomic absorption spectroscopy | atomic emission spectroscopy | inductively coupled plasme emission spectroscopy | icpms | icpes | atomic fluorescence spectroscopy | comparison of elemental analysis techniques | principles of mass spectroscopy | electron impact mass spectroscopy | chemical ionisation mass spectroscopy | quadrupole mass spectroscopy | time-of-flight mass analysers | ion-trap mass analysers | off-line sampling systems | at-line sampling systems | on-line sampling systems | in-line sampling systems | performance characteristics of analytical techniques | flow injection analysis | fia | process gc | process ir | process ms | process uv/visible | quality management | quality assurance | qa | vam principles | quality control | qc | analytical method validation | analytical method performance characteristics | sampling of solids | liquids and gases | measurement of ph | karl fischer titration | uv/visible spectroscopy | beer's law | beer-lambert law | deviations from beer's law | mid ir spectroscopy | near ir spectroscopy | raman spectroscopy | fourier transform spectroscopies | x-ray methods | x-ray fluorescence spectroscopy | gc-ms | lc-ms | Physical sciences | F000License

Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-nd/2.0/uk/ http://creativecommons.org/licenses/by-nc-nd/2.0/uk/Site sourced from

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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 frameworkLicense

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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Includes audio/video content: AV special element video. This course surveys techniques to fabricate and analyze submicron and nanometer structures, with applications. Optical and electron microscopy is reviewed. Additional topics that are covered include: surface characterization, preparation, and measurement techniques, resist technology, optical projection, interferometric, X-ray, ion, and electron lithography; Aqueous, ion, and plasma etching techniques; lift-off and electroplating; and ion implantation. Applications in microelectronics, microphotonics, information storage, and nanotechnology will also be explored.AcknowledgementsThe Instructors would like to thank Bob Barsotti, Bryan Cord, and Ben Wunsch for their work on the Atomic Force Microscope video. They would also like to thank Includes audio/video content: AV special element video. This course surveys techniques to fabricate and analyze submicron and nanometer structures, with applications. Optical and electron microscopy is reviewed. Additional topics that are covered include: surface characterization, preparation, and measurement techniques, resist technology, optical projection, interferometric, X-ray, ion, and electron lithography; Aqueous, ion, and plasma etching techniques; lift-off and electroplating; and ion implantation. Applications in microelectronics, microphotonics, information storage, and nanotechnology will also be explored.AcknowledgementsThe Instructors would like to thank Bob Barsotti, Bryan Cord, and Ben Wunsch for their work on the Atomic Force Microscope video. They would also like to thankSubjects

submicron and nanometer structures | submicron and nanometer structures | optical and electron microscopy | optical and electron microscopy | Surface characterization | Surface characterization | preparation | preparation | and measurement techniques | and measurement techniques | Resist technology | Resist technology | optical projection | optical projection | interferometric | interferometric | X-ray | X-ray | ion | ion | and electron lithography | and electron lithography | Aqueous | Aqueous | and plasma etching techniques | and plasma etching techniques | Lift-off and electroplating | Lift-off and electroplating | Ion implantation | Ion implantation | microelectronics | microelectronics | microphotonics | microphotonics | information storage | information storage | and nanotechnology | and nanotechnologyLicense

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 metadata5.311 Introductory Chemical Experimentation (MIT) 5.311 Introductory Chemical Experimentation (MIT)

Description

5.311 is the first of a three-term laboratory subject sequence for chemistry majors. Experimental work emphasizes development of fundamental laboratory skills and techniques: volumetric and colorimetric analysis; nuclear magnetic resonance; preparation, purification, and characterization of chemical substances; and data analysis. Acknowledgements The experiments for 5.311 have evolved over a period of many years and include contributions from past instructors, course textbooks, and others affiliated with the course. Thus for many of the lab documents, no single source can be attributed. WARNING NOTICE The experiments described in these materials are potentially hazardous and require a high level of safety training, special facilities and equipment, and supervision by appropriate individual 5.311 is the first of a three-term laboratory subject sequence for chemistry majors. Experimental work emphasizes development of fundamental laboratory skills and techniques: volumetric and colorimetric analysis; nuclear magnetic resonance; preparation, purification, and characterization of chemical substances; and data analysis. Acknowledgements The experiments for 5.311 have evolved over a period of many years and include contributions from past instructors, course textbooks, and others affiliated with the course. Thus for many of the lab documents, no single source can be attributed. WARNING NOTICE The experiments described in these materials are potentially hazardous and require a high level of safety training, special facilities and equipment, and supervision by appropriate individualSubjects

introductory chemistry lab | introductory chemistry lab | chemistry lab techniques | chemistry lab techniques | chemistry laboratory techniques | chemistry laboratory techniques | NMR | NMR | ferrocene | ferrocene | kinetics | kinetics | proton NMR | proton NMR | aromatic carboxylic acid | aromatic carboxylic acid | identifying unknown compounds | identifying unknown compounds | acetylferrocene | acetylferrocene | synthesis | synthesis | chromatography | chromatography | TLC | TLC | sublimation | sublimationLicense

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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A large proportion of contemporary research on organizations, strategy and management relies on quantitative research methods. This course is designed to provide an introduction to some of the most commonly used quantitative techniques, including logit/probit models, count models, event history models, and pooled cross-section techniques. A large proportion of contemporary research on organizations, strategy and management relies on quantitative research methods. This course is designed to provide an introduction to some of the most commonly used quantitative techniques, including logit/probit models, count models, event history models, and pooled cross-section techniques.Subjects

contemporary research on organizations | contemporary research on organizations | strategy and management | strategy and management | quantitative research methods | quantitative research methods | quantitative techniques | quantitative techniques | including logit/probit models | including logit/probit models | count models | count models | event history models | event history models | pooled cross-section techniques | pooled cross-section techniquesLicense

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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This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.Subjects

programming languages | programming languages | techniques used by physical scientists | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++ | C++ | Matlab | Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification | methods of dissemination and verificationLicense

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 metadata11.433J Real Estate Economics (MIT) 11.433J Real Estate Economics (MIT)

Description

This course, offered by the MIT Center for Real Estate, focuses on developing an understanding of the macroeconomic factors that shape and influence markets for real property. We will develop the theory of land markets and locational choice. The material covered includes studies of changing economic activities, demographic trends, transportation and local government behavior as they affect real estate. This course, offered by the MIT Center for Real Estate, focuses on developing an understanding of the macroeconomic factors that shape and influence markets for real property. We will develop the theory of land markets and locational choice. The material covered includes studies of changing economic activities, demographic trends, transportation and local government behavior as they affect real estate.Subjects

real estate; property; macroeconomic factors; supply and demand; market cycles; land markets; demographic trends; transportation; government regulation; real estate market; demographic analysis; regional growth; residential construction; new home building; commercial construction; retail stores; urban location theory; predicting demand; modeling techniques; urban economics; land use; urban growth; residential development; gentrification; zoning; property taxes; neighboorhood effects | real estate; property; macroeconomic factors; supply and demand; market cycles; land markets; demographic trends; transportation; government regulation; real estate market; demographic analysis; regional growth; residential construction; new home building; commercial construction; retail stores; urban location theory; predicting demand; modeling techniques; urban economics; land use; urban growth; residential development; gentrification; zoning; property taxes; neighboorhood effects | real estate | real estate | property | property | macroeconomic factors | macroeconomic factors | supply and demand | supply and demand | market cycles | market cycles | land markets | land markets | demographic trends | demographic trends | transportation | transportation | government regulation | government regulation | real estate market | real estate market | demographic analysis | demographic analysis | regional growth | regional growth | residential construction | residential construction | new home building | new home building | commercial construction | commercial construction | retail stores | retail stores | urban location theory | urban location theory | predicting demand | predicting demand | modeling techniques | modeling techniques | urban economics | urban economics | land use | land use | urban growth | urban growth | residential development | residential development | gentrification | gentrification | zoning | zoning | property taxes | property taxes | neighboorhood effects | neighboorhood effectsLicense

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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6.831/6.813 examines human-computer interaction in the context of graphical user interfaces. The course covers human capabilities, design principles, prototyping techniques, evaluation techniques, and the implementation of graphical user interfaces. Deliverables include short programming assignments and a semester-long group project. Students taking the graduate version also have readings from current literature and additional assignments. 6.831/6.813 examines human-computer interaction in the context of graphical user interfaces. The course covers human capabilities, design principles, prototyping techniques, evaluation techniques, and the implementation of graphical user interfaces. Deliverables include short programming assignments and a semester-long group project. Students taking the graduate version also have readings from current literature and additional assignments.Subjects

human-computer interaction | human-computer interaction | user interfaces | user interfaces | human capabilities | human capabilities | design principles | design principles | prototyping techniques | prototyping techniques | evaluation techniques | evaluation techniquesLicense

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 metadata10.34 Numerical Methods Applied to Chemical Engineering (MIT)

Description

This course focuses on the use of modern computational and mathematical techniques in chemical engineering. Starting from a discussion of linear systems as the basic computational unit in scientific computing, methods for solving sets of nonlinear algebraic equations, ordinary differential equations, and differential-algebraic (DAE) systems are presented. Probability theory and its use in physical modeling is covered, as is the statistical analysis of data and parameter estimation. The finite difference and finite element techniques are presented for converting the partial differential equations obtained from transport phenomena to DAE systems. The use of these techniques will be demonstrated throughout the course in the MATLAB® computing environment.Subjects

Matlab | modern computational techniques in chemical engineering | mathematical techniques in chemical engineering | linear systems | scientific computing | solving sets of nonlinear algebraic equations | solving ordinary differential equations | solving differential-algebraic (DAE) systems | probability theory | use of probability theory in physical modeling | statistical analysis of data estimation | statistical analysis of parameter estimation | finite difference techniques | finite element techniques | converting partial differential equationsLicense

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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Advanced Analytic Methods in Science and Engineering is a comprehensive treatment of the advanced methods of applied mathematics. It was designed to strengthen the mathematical abilities of graduate students and train them to think on their own. Advanced Analytic Methods in Science and Engineering is a comprehensive treatment of the advanced methods of applied mathematics. It was designed to strengthen the mathematical abilities of graduate students and train them to think on their own.Subjects

elementary methods complex analysis | elementary methods complex analysis | ordinary differential equations | ordinary differential equations | partial differential equations | partial differential equations | expansions around regular irregular singular points | expansions around regular irregular singular points | asymptotic evaluation integrals | asymptotic evaluation integrals | regular perturbations | regular perturbations | WKB method | WKB method | multiple scale method | multiple scale method | boundary-layer techniques. | boundary-layer techniques. | asymptotic evaluation integrals | regular perturbations | asymptotic evaluation integrals | regular perturbations | boundary-layer techniques | boundary-layer techniquesLicense

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 covers a variety of topics concerning superconducting magnets, including thermodynamic and transport properties of aqueous and nonaqueous electrolytes, the electrode/electrolyte interface, and the kinetics of electrode processes. It also covers electrochemical characterization with regards to d.c. techniques (controlled potential, controlled current) and a.c. techniques (voltametry and impedance spectroscopy). Applications of the following will also be discussed: electrowinning, electrorefining, electroplating, and electrosynthesis, as well as electrochemical power sources (batteries and fuel cells). This course covers a variety of topics concerning superconducting magnets, including thermodynamic and transport properties of aqueous and nonaqueous electrolytes, the electrode/electrolyte interface, and the kinetics of electrode processes. It also covers electrochemical characterization with regards to d.c. techniques (controlled potential, controlled current) and a.c. techniques (voltametry and impedance spectroscopy). Applications of the following will also be discussed: electrowinning, electrorefining, electroplating, and electrosynthesis, as well as electrochemical power sources (batteries and fuel cells).Subjects

Thermodynamic and transport properties of aqueous and nonaqueous electrolytes | Thermodynamic and transport properties of aqueous and nonaqueous electrolytes | electrode/electrolyte interface | electrode/electrolyte interface | Kinetics of electrode processes | Kinetics of electrode processes | Electrochemical characterization | Electrochemical characterization | d.c. techniques (controlled potential | controlled current) | d.c. techniques (controlled potential | controlled current) | a.c. techniques (voltametry and impedance spectroscopy) | a.c. techniques (voltametry and impedance spectroscopy) | electrowinning | electrowinning | electrorefining | electrorefining | electroplating | electroplating | electrosynthesis | electrosynthesis | electrochemical power sources (batteries and fuel cells) | electrochemical power sources (batteries and fuel cells)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.htmSite sourced from

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See all metadata6.341 Discrete-Time Signal Processing (MIT) 6.341 Discrete-Time Signal Processing (MIT)

Description

This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications. Acknowledgements I would like to express my thanks to Thomas Baran, Myung Jin Choi, and Xiaomeng Shi for compiling the lecture notes on this site from my individual lectures and handouts and their class notes during the semest This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications. Acknowledgements I would like to express my thanks to Thomas Baran, Myung Jin Choi, and Xiaomeng Shi for compiling the lecture notes on this site from my individual lectures and handouts and their class notes during the semestSubjects

discrete time signals and systems | discrete time signals and systems | discrete-time processing of continuous-time signals | discrete-time processing of continuous-time signals | decimation | decimation | interpolation | interpolation | sampling rate conversion | sampling rate conversion | Flowgraph structures | Flowgraph structures | time- and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters | time- and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters | linear prediction | linear prediction | Discrete Fourier transform | Discrete Fourier transform | FFT algorithm | FFT algorithm | Short-time Fourier analysis and filter banks | Short-time Fourier analysis and filter banks | Multirate techniques | Multirate techniques | Hilbert transforms | Hilbert transforms | Cepstral analysis | Cepstral analysisLicense

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 metadata10.34 Numerical Methods Applied to Chemical Engineering (MIT)

Description

Numerical methods for solving problems arising in heat and mass transfer, fluid mechanics, chemical reaction engineering, and molecular simulation. Topics: numerical linear algebra, solution of nonlinear algebraic equations and ordinary differential equations, solution of partial differential equations (e.g. Navier-Stokes), numerical methods in molecular simulation (dynamics, geometry optimization). All methods are presented within the context of chemical engineering problems. Familiarity with structured programming is assumed. The examples will use MATLAB®. Acknowledgements The instructor would like to thank Robert Ashcraft, Sandeep Sharma, David Weingeist, and Nikolay Zaborenko for their work in preparing materials for this course site.Subjects

Matlab | modern computational techniques in chemical engineering | mathematical techniques in chemical engineering | linear systems | scientific computing | solving sets of nonlinear algebraic equations | solving ordinary differential equations | solving differential-algebraic (DAE) systems | probability theory | use of probability theory in physical modeling | statistical analysis of data estimation | statistical analysis of parameter estimation | finite difference techniques | finite element techniques | converting partial differential equations | Navier-Stokes equationsLicense

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This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. Students first learn the basic usage of each language, common types of problems encountered, and techniques for solving a variety of problems encountered in contemporary research: examination of data with visualization techniques, numerical analysis, and methods of dissemination and verification. No prior programming experience is required.Technical RequirementsAny number of development tools can be used to compile and run the .c and .f files found on this course site. C++ compiler is required to This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. Students first learn the basic usage of each language, common types of problems encountered, and techniques for solving a variety of problems encountered in contemporary research: examination of data with visualization techniques, numerical analysis, and methods of dissemination and verification. No prior programming experience is required.Technical RequirementsAny number of development tools can be used to compile and run the .c and .f files found on this course site. C++ compiler is required toSubjects

programming languages | techniques used by physical scientists | programming languages | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++ | C++ | Matlab | Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification | methods of dissemination and verification | algorithms | algorithms | formula | formula | formulae | formulae | computer programs | computer programs | graphics | graphics | computing languages | computing languages | structure | structure | documentation | documentation | program interface | program interface | syntax | syntax | advanced modeling | advanced modeling | simulation systems | simulation 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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This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.Subjects

programming languages | programming languages | techniques used by physical scientists | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++ | C++ | Matlab | Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification | methods of dissemination and verificationLicense

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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A large proportion of contemporary research on organizations, strategy and management relies on quantitative research methods. This course is designed to provide an introduction to some of the most commonly used quantitative techniques, including logit/probit models, count models, event history models, and pooled cross-section techniques. A large proportion of contemporary research on organizations, strategy and management relies on quantitative research methods. This course is designed to provide an introduction to some of the most commonly used quantitative techniques, including logit/probit models, count models, event history models, and pooled cross-section techniques.Subjects

contemporary research on organizations | contemporary research on organizations | strategy and management | strategy and management | quantitative research methods | quantitative research methods | quantitative techniques | quantitative techniques | including logit/probit models | including logit/probit models | count models | count models | event history models | event history models | pooled cross-section techniques | pooled cross-section techniquesLicense

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 metadata15.099 Readings in Optimization (MIT) 15.099 Readings in Optimization (MIT)

Description

In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention. In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.Subjects

deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization | deterministic optimization | algorithms | algorithms | stochastic processes | stochastic processes | random number generation | random number generation | simplex method | simplex method | nonlinear | nonlinear | convex | convex | complexity analysis | complexity analysis | semidefinite programming | semidefinite programming | heuristic | heuristic | global optimization | global optimization | Las Vegas algorithm | Las Vegas algorithm | randomized algorithm | randomized algorithm | linear programming | linear programming | search techniques | search techniques | hit and run | hit and run | NP-hard | NP-hard | approximation | approximationLicense

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 metadata1.561 Motion Based Design (MIT) 1.561 Motion Based Design (MIT)

Description

This course presents a rational basis for the preliminary design of motion-sensitive structures. Topics covered include: analytical and numerical techniques for establishing the optimal stiffness distribution, the role of damping in controlling motion, tuned mass dampers, base isolation systems, and active structural control. Examples illustrating the application of the motion-based design paradigm to building structures subjected to seismic excitation are discussed. This course presents a rational basis for the preliminary design of motion-sensitive structures. Topics covered include: analytical and numerical techniques for establishing the optimal stiffness distribution, the role of damping in controlling motion, tuned mass dampers, base isolation systems, and active structural control. Examples illustrating the application of the motion-based design paradigm to building structures subjected to seismic excitation are discussed.Subjects

preliminary design | preliminary design | motion-sensitive structures | motion-sensitive structures | analytical techniques | analytical techniques | numerical techniques | numerical techniques | optimal stiffness distribution | optimal stiffness distribution | damping | damping | controlling motion | controlling motion | tuned mass dampers | tuned mass dampers | base isolation systems | base isolation systems | active structural control | active structural control | building structures | building structures | wind excitation | wind excitation | seismic excitation | seismic excitation | building design | building design | numerical analysis | numerical analysis | motion control | motion control | motion-based design | motion-based design | safety | safety | serviceability | serviceability | loadings | loadings | optimal stiffness | optimal stiffness | optimal damping | optimal damping | base isolation | base isolationLicense

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 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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See all metadata1.124J Foundations of Software Engineering (MIT) 1.124J Foundations of Software Engineering (MIT)

Description

This is a foundation subject in modern software development techniques for engineering and information technology. The design and development of component-based software (using C# and .NET) is covered; data structures and algorithms for modeling, analysis, and visualization; basic problem-solving techniques; web services; and the management and maintenance of software. Includes a treatment of topics such as sorting and searching algorithms; and numerical simulation techniques. Foundation for in-depth exploration of image processing, computational geometry, finite element methods, network methods and e-business applications. This course is a core requirement for the Information Technology M. Eng. program. This class was also offered in Course 13 (Department of Ocean Engineering) as 13.470J. This is a foundation subject in modern software development techniques for engineering and information technology. The design and development of component-based software (using C# and .NET) is covered; data structures and algorithms for modeling, analysis, and visualization; basic problem-solving techniques; web services; and the management and maintenance of software. Includes a treatment of topics such as sorting and searching algorithms; and numerical simulation techniques. Foundation for in-depth exploration of image processing, computational geometry, finite element methods, network methods and e-business applications. This course is a core requirement for the Information Technology M. Eng. program. This class was also offered in Course 13 (Department of Ocean Engineering) as 13.470J.Subjects

modern software development | modern software development | engineering and information technology | engineering and information technology | component-based software | component-based software | C# | C# | .NET | .NET | data structures | data structures | algorithms for modeling | algorithms for modeling | analysis | analysis | visualization | visualization | basic problem-solving techniques | basic problem-solving techniques | web services | web services | management and maintenance of software | management and maintenance of software | sorting | sorting | searching | searching | algorithms | algorithms | numerical simulation techniques | numerical simulation techniques | image processing | image processing | computational geometry | computational geometry | finite element methods | finite element methods | network methods | network methods | e-business applications | e-business applications | classes | classes | objects | objects | inheritance | inheritance | virtual functions | virtual functions | abstract classes | abstract classes | polymorphism | polymorphism | Java applications | Java applications | applets | applets | Abstract Windowing Toolkit | Abstract Windowing Toolkit | Graphics | Graphics | Threads | Threads | Java | Java | C++ | C++ | information technology | information technology | engineering | engineering | modeling algorithms | modeling algorithms | basic problem-solving | basic problem-solving | software management | software management | software maintenance | software maintenance | searching algorithms | searching algorithms | numerical simulation | numerical simulation | object oriented programming | object oriented programming | 13.470J | 13.470J | 1.124 | 1.124 | 2.159 | 2.159 | 13.470 | 13.470License

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 metadata10.34 Numerical Methods Applied to Chemical Engineering (MIT)

Description

This course focuses on the use of modern computational and mathematical techniques in chemical engineering. Starting from a discussion of linear systems as the basic computational unit in scientific computing, methods for solving sets of nonlinear algebraic equations, ordinary differential equations, and differential-algebraic (DAE) systems are presented. Probability theory and its use in physical modeling is covered, as is the statistical analysis of data and parameter estimation. The finite difference and finite element techniques are presented for converting the partial differential equations obtained from transport phenomena to DAE systems. The use of these techniques will be demonstrated throughout the course in the MATLAB® computing environment.Subjects

Matlab | modern computational techniques in chemical engineering | mathematical techniques in chemical engineering | linear systems | scientific computing | solving sets of nonlinear algebraic equations | solving ordinary differential equations | solving differential-algebraic (DAE) systems | probability theory | use of probability theory in physical modeling | statistical analysis of data estimation | statistical analysis of parameter estimation | finite difference techniques | finite element techniques | converting partial differential equationsLicense

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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