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Control (MIT) Control (MIT)

Description

6.241 examines linear, discrete- and continuous-time, and multi-input-output systems in control and related areas. Least squares and matrix perturbation problems are considered. Topics covered include: state-space models, modes, stability, controllability, observability, transfer function matrices, poles and zeros, minimality, internal stability of interconnected systems, feedback compensators, state feedback, optimal regulation, observers, observer-based compensators, measures of control performance, and robustness issues using singular values of transfer functions. Nonlinear systems are also introduced. 6.241 examines linear, discrete- and continuous-time, and multi-input-output systems in control and related areas. Least squares and matrix perturbation problems are considered. Topics covered include: state-space models, modes, stability, controllability, observability, transfer function matrices, poles and zeros, minimality, internal stability of interconnected systems, feedback compensators, state feedback, optimal regulation, observers, observer-based compensators, measures of control performance, and robustness issues using singular values of transfer functions. Nonlinear systems are also introduced.

Subjects

control | control | linear | linear | discrete | discrete | continuous-time | continuous-time | multi-input-output | multi-input-output | least squares | least squares | matrix perturbation | matrix perturbation | state-space models | stability | controllability | observability | transfer function matrices | poles | state-space models | stability | controllability | observability | transfer function matrices | poles | zeros | zeros | minimality | minimality | feedback | feedback | compensators | compensators | state feedback | state feedback | optimal regulation | optimal regulation | observers | transfer functions | observers | transfer functions | nonlinear systems | nonlinear systems | linear systems | linear systems

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18.034 Honors Differential Equations (MIT) 18.034 Honors Differential Equations (MIT)

Description

This course covers the same material as 18.03 with more emphasis on theory. Topics include first order equations, separation, initial value problems, systems, linear equations, independence of solutions, undetermined coefficients, and singular points and periodic orbits for planar systems. This course covers the same material as 18.03 with more emphasis on theory. Topics include first order equations, separation, initial value problems, systems, linear equations, independence of solutions, undetermined coefficients, and singular points and periodic orbits for planar systems.

Subjects

First order equations | First order equations | Separation | Separation | initial value problems | initial value problems | Systems | Systems | linear equations | linear equations | independence of solutions | independence of solutions | undetermined coefficients | undetermined coefficients | Singular points | Singular points | periodic orbits for planar systems | periodic orbits for planar systems | first order ode's | first order ode's | second order ode's | second order ode's | fourier series | fourier series | laplace transform | laplace transform | linear systems | linear systems | nonlinear systems | nonlinear systems | constant coefficients | constant coefficients

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18.335J Introduction to Numerical Methods (MIT) 18.335J Introduction to Numerical Methods (MIT)

Description

This course offers an advanced introduction to numerical linear algebra. Topics include direct and iterative methods for linear systems, eigenvalue decompositions and QR/SVD factorizations, stability and accuracy of numerical algorithms, the IEEE floating point standard, sparse and structured matrices, preconditioning, linear algebra software. Problem sets require some knowledge of MATLAB®. This course offers an advanced introduction to numerical linear algebra. Topics include direct and iterative methods for linear systems, eigenvalue decompositions and QR/SVD factorizations, stability and accuracy of numerical algorithms, the IEEE floating point standard, sparse and structured matrices, preconditioning, linear algebra software. Problem sets require some knowledge of MATLAB®.

Subjects

numerical linear algebra | numerical linear algebra | linear systems | linear systems | eigenvalue decomposition | eigenvalue decomposition | QR/SVD factorization | QR/SVD factorization | numerical algorithms | numerical algorithms | IEEE floating point standard | IEEE floating point standard | sparse matrices | sparse matrices | structured matrices | structured matrices | preconditioning | preconditioning | linear algebra software | linear algebra software | Matlab | Matlab

License

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18.03 Differential Equations (MIT) 18.03 Differential Equations (MIT)

Description

Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time. Topics include: Solution of first-order ODE's by analytical, graphical and numerical methods; Linear ODE's, especially second order with constant coefficients; Undetermined coefficients and variation of parameters; Sinusoidal and exponential signals: oscillations, damping, resonance; Complex numbers and exponentials; Fourier series, periodic solutions; Delta functions, convolution, and Laplace transform methods; Matrix and first order linear systems: eigenvalues and Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time. Topics include: Solution of first-order ODE's by analytical, graphical and numerical methods; Linear ODE's, especially second order with constant coefficients; Undetermined coefficients and variation of parameters; Sinusoidal and exponential signals: oscillations, damping, resonance; Complex numbers and exponentials; Fourier series, periodic solutions; Delta functions, convolution, and Laplace transform methods; Matrix and first order linear systems: eigenvalues and

Subjects

Ordinary Differential Equations | Ordinary Differential Equations | ODE | ODE | modeling physical systems | modeling physical systems | first-order ODE's | first-order ODE's | Linear ODE's | Linear ODE's | second order ODE's | second order ODE's | second order ODE's with constant coefficients | second order ODE's with constant coefficients | Undetermined coefficients | Undetermined coefficients | variation of parameters | variation of parameters | Sinusoidal signals | Sinusoidal signals | exponential signals | exponential signals | oscillations | oscillations | damping | damping | resonance | resonance | Complex numbers and exponentials | Complex numbers and exponentials | Fourier series | Fourier series | periodic solutions | periodic solutions | Delta functions | Delta functions | convolution | convolution | Laplace transform methods Matrix systems | Laplace transform methods Matrix systems | first order linear systems | first order linear systems | eigenvalues and eigenvectors | eigenvalues and eigenvectors | Non-linear autonomous systems | Non-linear autonomous systems | critical point analysis | critical point analysis | phase plane diagrams | phase plane diagrams

License

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2.853 Manufacturing Systems I: Analytical Methods and Flow Models (MIT) 2.853 Manufacturing Systems I: Analytical Methods and Flow Models (MIT)

Description

Provides ways to conceptualize and analyze manufacturing systems and supply chains in terms of material flow, information flow, capacities, and flow times. Fundamental building blocks: inventory and queuing models, forecasting and uncertainty, optimization, process analysis, linear systems and system dynamics. Factory planning: flow planning, bottleneck characterization, buffer and batch-size tactics, seasonal planning, dynamics and learning for various process flow topologies and for various market contexts.Technical RequirementsMicrosoft® Excel software is recommended for viewing the .xls files found on this course site. Free  Microsoft® Excel viewer software can also be used to view the .xls files.Microsoft® is a registered trademark Provides ways to conceptualize and analyze manufacturing systems and supply chains in terms of material flow, information flow, capacities, and flow times. Fundamental building blocks: inventory and queuing models, forecasting and uncertainty, optimization, process analysis, linear systems and system dynamics. Factory planning: flow planning, bottleneck characterization, buffer and batch-size tactics, seasonal planning, dynamics and learning for various process flow topologies and for various market contexts.Technical RequirementsMicrosoft® Excel software is recommended for viewing the .xls files found on this course site. Free  Microsoft® Excel viewer software can also be used to view the .xls files.Microsoft® is a registered trademark

Subjects

manufacturing systems | manufacturing systems | supply chains | supply chains | material flow | material flow | information flow | information flow | capacities | capacities | flow times | flow times | Fundamental building blocks | Fundamental building blocks | inventory | inventory | queuing models | queuing models | forecasting | forecasting | uncertainty | uncertainty | optimization | optimization | process analysis | process analysis | linear systems | linear systems | system dynamics | system dynamics | Factory planning | Factory planning | flow planning | flow planning | bottleneck characterization | bottleneck characterization | buffer | buffer | batch-size tactics | batch-size tactics | seasonal planning | seasonal planning | process flow topologies | process flow topologies | market contexts | market contexts

License

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9.29J Introduction to Computational Neuroscience (MIT) 9.29J Introduction to Computational Neuroscience (MIT)

Description

Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

Subjects

neural coding | neural coding | dynamics | dynamics | convolution | convolution | correlation | correlation | linear systems | linear systems | Fourier analysis | Fourier analysis | signal detection theory | signal detection theory | probability theory | probability theory | information theory | information theory | neural excitability | neural excitability | stochastic models | stochastic models | ion channels | ion channels | cable theory | cable theory | 9.29 | 9.29 | 8.261 | 8.261

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18.03 Differential Equations (MIT) 18.03 Differential Equations (MIT)

Description

Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time. Topics include: Solution of first-order ODE's by analytical, graphical and numerical methods; Linear ODE's, especially second order with constant coefficients; Undetermined coefficients and variation of parameters; Sinusoidal and exponential signals: oscillations, damping, resonance; Complex numbers and exponentials; Fourier series, periodic solutions; Delta functions, convolution, and Laplace transform methods; Matrix and first order linear systems: eigenvalues and Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time. Topics include: Solution of first-order ODE's by analytical, graphical and numerical methods; Linear ODE's, especially second order with constant coefficients; Undetermined coefficients and variation of parameters; Sinusoidal and exponential signals: oscillations, damping, resonance; Complex numbers and exponentials; Fourier series, periodic solutions; Delta functions, convolution, and Laplace transform methods; Matrix and first order linear systems: eigenvalues and

Subjects

Ordinary Differential Equations | Ordinary Differential Equations | ODE | ODE | modeling physical systems | modeling physical systems | first-order ODE's | first-order ODE's | Linear ODE's | Linear ODE's | second order ODE's | second order ODE's | second order ODE's with constant coefficients | second order ODE's with constant coefficients | Undetermined coefficients | Undetermined coefficients | variation of parameters | variation of parameters | Sinusoidal signals | Sinusoidal signals | exponential signals | exponential signals | oscillations | oscillations | damping | damping | resonance | resonance | Complex numbers and exponentials | Complex numbers and exponentials | Fourier series | Fourier series | periodic solutions | periodic solutions | Delta functions | Delta functions | convolution | convolution | Laplace transform methods | Laplace transform methods | Matrix systems | Matrix systems | first order linear systems | first order linear systems | eigenvalues and eigenvectors | eigenvalues and eigenvectors | Non-linear autonomous systems | Non-linear autonomous systems | critical point analysis | critical point analysis | phase plane diagrams | phase plane diagrams | constant coefficients | constant coefficients | complex numbers | complex numbers | exponentials | exponentials | eigenvalues | eigenvalues | eigenvectors | eigenvectors

License

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17.537 Politics and Policy in Contemporary Japan (MIT) 17.537 Politics and Policy in Contemporary Japan (MIT)

Description

This subject is designed for upper level undergraduates and graduate students as an introduction to politics and the policy process in modern Japan. The semester is divided into two parts. After a two-week general introduction to Japan and to the dominant approaches to the study of Japanese history, politics and society, we will begin exploring five aspects of Japanese politics: party politics, electoral politics, interest group politics, bureaucratic politics, and policy, which will be broken up into seven additional sections. We will try to understand the ways in which the actors and institutions identified in the first part of the semester affect the policy process across a variety of issues areas. This subject is designed for upper level undergraduates and graduate students as an introduction to politics and the policy process in modern Japan. The semester is divided into two parts. After a two-week general introduction to Japan and to the dominant approaches to the study of Japanese history, politics and society, we will begin exploring five aspects of Japanese politics: party politics, electoral politics, interest group politics, bureaucratic politics, and policy, which will be broken up into seven additional sections. We will try to understand the ways in which the actors and institutions identified in the first part of the semester affect the policy process across a variety of issues areas.

Subjects

finite element methods | finite element methods | solids | solids | structures | structures | fluid mechanics | fluid mechanics | heat transfer | heat transfer | equilibrium equations | equilibrium equations | direct integration | direct integration | mode superposition | mode superposition | eigensolution techniques | eigensolution techniques | frequencies | frequencies | mode shapes | mode shapes | statics | statics | dynamics | dynamics | nonlinear systems | nonlinear systems | wave propagation | wave propagation | Japan | Japan | politics | politics | policy | policy | contemporary Japan | contemporary Japan | electoral politics | electoral politics | interest group politics | interest group politics | party politics | party politics | bureaucratic politics | bureaucratic politics | social policy | social policy | foreign policy | foreign policy | defense policy | defense policy | energy policy | energy policy | science and technology policy | science and technology policy | industrial policy | industrial policy | trade policy | trade policy

License

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2.086 Numerical Computation for Mechanical Engineers (MIT) 2.086 Numerical Computation for Mechanical Engineers (MIT)

Description

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

Subjects

MATLAB | MATLAB | numerical analysis | numerical analysis | programming | programming | physical modeling | physical modeling | calculus | calculus | linear algebra | linear algebra | Monte Carlo Method | Monte Carlo Method | differential equations | differential equations | nonlinear systems | nonlinear systems

License

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18.03SC Differential Equations (MIT) 18.03SC Differential Equations (MIT)

Description

Includes audio/video content: AV lectures. The laws of nature are expressed as differential equations. Scientists and engineers must know how to model the world in terms of differential equations, and how to solve those equations and interpret the solutions. This course focuses on the equations and techniques most useful in science and engineering. Includes audio/video content: AV lectures. The laws of nature are expressed as differential equations. Scientists and engineers must know how to model the world in terms of differential equations, and how to solve those equations and interpret the solutions. This course focuses on the equations and techniques most useful in science and engineering.

Subjects

Ordinary Differential Equations | Ordinary Differential Equations | ODE | ODE | modeling physical systems | modeling physical systems | first-order ODE's | first-order ODE's | Linear ODE's | Linear ODE's | second order ODE's | second order ODE's | second order ODE's with constant coefficients | second order ODE's with constant coefficients | Undetermined coefficients | Undetermined coefficients | variation of parameters | variation of parameters | Sinusoidal signals | Sinusoidal signals | exponential signals | exponential signals | oscillations | oscillations | damping | damping | resonance | resonance | Complex numbers and exponentials | Complex numbers and exponentials | Fourier series | Fourier series | periodic solutions | periodic solutions | Delta functions | Delta functions | convolution | convolution | Laplace transform methods | Laplace transform methods | Matrix systems | Matrix systems | first order linear systems | first order linear systems | eigenvalues and eigenvectors | eigenvalues and eigenvectors | Non-linear autonomous systems | Non-linear autonomous systems | critical point analysis | critical point analysis | phase plane diagrams | phase plane diagrams

License

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18.03 Differential Equations (MIT) 18.03 Differential Equations (MIT)

Description

Includes audio/video content: AV lectures. Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time. Includes audio/video content: AV lectures. Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time.

Subjects

Ordinary Differential Equations | Ordinary Differential Equations | ODE | ODE | modeling physical systems | modeling physical systems | first-order ODE's | first-order ODE's | Linear ODE's | Linear ODE's | second order ODE's | second order ODE's | second order ODE's with constant coefficients | second order ODE's with constant coefficients | Undetermined coefficients | Undetermined coefficients | variation of parameters | variation of parameters | Sinusoidal signals | Sinusoidal signals | exponential signals | exponential signals | oscillations | oscillations | damping | damping | resonance | resonance | Complex numbers and exponentials | Complex numbers and exponentials | Fourier series | Fourier series | periodic solutions | periodic solutions | Delta functions | Delta functions | convolution | convolution | Laplace transform methods | Laplace transform methods | Matrix systems | Matrix systems | first order linear systems | first order linear systems | eigenvalues and eigenvectors | eigenvalues and eigenvectors | Non-linear autonomous systems | Non-linear autonomous systems | critical point analysis | critical point analysis | phase plane diagrams | phase plane diagrams

License

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2.29 Numerical Fluid Mechanics (MIT) 2.29 Numerical Fluid Mechanics (MIT)

Description

This course will provide students with an introduction to numerical methods and MATLAB®. Topics covered throughout the course will include: errors, condition numbers and roots of equations; Navier-Stokes; direct and iterative methods for linear systems; finite differences for elliptic, parabolic and hyperbolic equations; Fourier decomposition, error analysis, and stability; high-order and compact finite-differences; finite volume methods; time marching methods; Navier-Stokes solvers; grid generation; finite volumes on complex geometries; finite element methods; spectral methods; boundary element and panel methods; turbulent flows; boundary layers; Lagrangian Coherent Structures. Subject includes a final research project. This course will provide students with an introduction to numerical methods and MATLAB®. Topics covered throughout the course will include: errors, condition numbers and roots of equations; Navier-Stokes; direct and iterative methods for linear systems; finite differences for elliptic, parabolic and hyperbolic equations; Fourier decomposition, error analysis, and stability; high-order and compact finite-differences; finite volume methods; time marching methods; Navier-Stokes solvers; grid generation; finite volumes on complex geometries; finite element methods; spectral methods; boundary element and panel methods; turbulent flows; boundary layers; Lagrangian Coherent Structures. Subject includes a final research project.

Subjects

errors | errors | condition numbers and roots of equations | condition numbers and roots of equations | Navier-Stokes | Navier-Stokes | direct and iterative methods for linear systems | direct and iterative methods for linear systems | finite differences for elliptic | finite differences for elliptic | parabolic and hyperbolic equations | parabolic and hyperbolic equations | Fourier decomposition | error analysis | and stability | Fourier decomposition | error analysis | and stability | high-order and compact finite-differences | high-order and compact finite-differences | finite volume methods | finite volume methods | time marching methods | time marching methods | Navier-Stokes solvers | Navier-Stokes solvers | grid generation | grid generation | finite volumes on complex geometries | finite volumes on complex geometries | finite element methods | finite element methods | spectral methods | spectral methods | boundary element and panel methods | boundary element and panel methods | turbulent flows | turbulent flows | boundary layers | boundary layers | Lagrangian Coherent Structures | Lagrangian Coherent Structures

License

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2.092 Finite Element Analysis of Solids and Fluids I (MIT) 2.092 Finite Element Analysis of Solids and Fluids I (MIT)

Description

This course introduces finite element methods for the analysis of solid, structural, fluid, field, and heat transfer problems. Steady-state, transient, and dynamic conditions are considered. Finite element methods and solution procedures for linear and nonlinear analyses are presented using largely physical arguments. The homework and a term project (for graduate students) involve use of the general purpose finite element analysis program ADINA. Applications include finite element analyses, modeling of problems, and interpretation of numerical results. This course introduces finite element methods for the analysis of solid, structural, fluid, field, and heat transfer problems. Steady-state, transient, and dynamic conditions are considered. Finite element methods and solution procedures for linear and nonlinear analyses are presented using largely physical arguments. The homework and a term project (for graduate students) involve use of the general purpose finite element analysis program ADINA. Applications include finite element analyses, modeling of problems, and interpretation of numerical results.

Subjects

finite element methods | finite element methods | solids | solids | structures | structures | fluid mechanics | fluid mechanics | heat transfer | heat transfer | equilibrium equations | equilibrium equations | direct integration | direct integration | mode superposition | mode superposition | eigensolution techniques | eigensolution techniques | frequencies | frequencies | mode shapes | mode shapes | statics | statics | dynamics | dynamics | nonlinear systems | nonlinear systems | wave propagation | wave propagation

License

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6.011 Introduction to Communication, Control, and Signal Processing (MIT) 6.011 Introduction to Communication, Control, and Signal Processing (MIT)

Description

This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection. This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.

Subjects

Input-output | Input-output | state-space models | state-space models | linear systems | linear systems | deterministic and random signals | deterministic and random signals | time- and transform-domain representations | time- and transform-domain representations | sampling | sampling | discrete-time processing | discrete-time processing | continuous-time signals | continuous-time signals | state feedback | state feedback | observers | observers | probabilistic models | probabilistic models | stochastic processes | stochastic processes | correlation functions | correlation functions | power spectra | power spectra | whitening filters | whitening filters | Detection | Detection | matched filters | matched filters | Least-mean square error estimation | Least-mean square error estimation | Wiener filtering | Wiener filtering

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6.245 Multivariable Control Systems (MIT) 6.245 Multivariable Control Systems (MIT)

Description

This course uses computer-aided design methodologies for synthesis of multivariable feedback control systems. Topics covered include: performance and robustness trade-offs; model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; and nonlinear effects. The assignments for the course comprise of computer-aided (MATLAB®) design problems. This course uses computer-aided design methodologies for synthesis of multivariable feedback control systems. Topics covered include: performance and robustness trade-offs; model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; and nonlinear effects. The assignments for the course comprise of computer-aided (MATLAB®) design problems.

Subjects

multivariable control systems | multivariable control systems | computer-aided design | computer-aided design | MATLAB | MATLAB | multivariable feedback control systems | multivariable feedback control systems | model-based compensators | model-based compensators | Q-parameterization | Q-parameterization | optimization | optimization | dynamic augmentation | dynamic augmentation | linear-quadratic optimization | linear-quadratic optimization | H-infinity controller design | H-infinity controller design | Mu-synthesis | Mu-synthesis | nonlinear systems | nonlinear systems | engineering design | engineering design

License

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6.243J Dynamics of Nonlinear Systems (MIT) 6.243J Dynamics of Nonlinear Systems (MIT)

Description

This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems. This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems.

Subjects

nonlinear systems | nonlinear systems | deterministic dynamical systems | deterministic dynamical systems | ordinary differential equations | ordinary differential equations | planar autonomous systems | planar autonomous systems | Picard iteration | Picard iteration | contraction mapping theorem | contraction mapping theorem | Bellman-Gronwall lemma | Bellman-Gronwall lemma | Lyapunov methods | Lyapunov methods | feedback linearization | feedback linearization | nonlinear circuits | nonlinear circuits | control systems | control systems | local controllability | local controllability | volume evolution | volume evolution | system analysis | system analysis | singular perturbations | singular perturbations | averaging | averaging | local behavior | local behavior | trajectories | trajectories | equilibria | equilibria | storage functions | storage functions | stability analysis | stability analysis | continuity | continuity | differential equations | differential equations | system models | system models | parameters | parameters | input/output | input/output | state-space | state-space | 16.337 | 16.337

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9.29J Introduction to Computational Neuroscience (MIT) 9.29J Introduction to Computational Neuroscience (MIT)

Description

This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. Visit the Seung Lab Web site. This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. Visit the Seung Lab Web site.

Subjects

neural coding | neural coding | dynamics | dynamics | convolution | convolution | correlation | correlation | linear systems | linear systems | Fourier analysis | Fourier analysis | signal detection theory | signal detection theory | probability theory | probability theory | information theory | information theory | neural excitability | neural excitability | stochastic models | stochastic models | ion channels | ion channels | cable theory | cable theory

License

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10.34 Numerical Methods Applied to Chemical Engineering (MIT) 10.34 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. 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 equations

License

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10.34 Numerical Methods Applied to Chemical Engineering (MIT) 10.34 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. 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 equations

License

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16.30 Feedback Control Systems (MIT) 16.30 Feedback Control Systems (MIT)

Description

This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to: Design controllers using state-space methods and analyze using classical tools. Understand impact of implementation issues (nonlinearity, delay). Indicate the robustness of your control design. Linearize a nonlinear system, and analyze stability. This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to: Design controllers using state-space methods and analyze using classical tools. Understand impact of implementation issues (nonlinearity, delay). Indicate the robustness of your control design. Linearize a nonlinear system, and analyze stability.

Subjects

control design | control design | control analysis | control analysis | state-space methods | state-space methods | linear systems | linear systems | estimation filters | estimation filters | dynamic output feedback | dynamic output feedback | full state feedback | full state feedback | state estimation | state estimation | output feedback | output feedback | nonlinear analysis | nonlinear analysis | model uncertainty | model uncertainty | robustness | robustness

License

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18.335J Introduction to Numerical Methods (MIT) 18.335J Introduction to Numerical Methods (MIT)

Description

This course offers an advanced introduction to numerical linear algebra. Topics include direct and iterative methods for linear systems, eigenvalue decompositions and QR/SVD factorizations, stability and accuracy of numerical algorithms, the IEEE floating point standard, sparse and structured matrices, preconditioning, linear algebra software. Problem sets require some knowledge of MATLAB®. This course offers an advanced introduction to numerical linear algebra. Topics include direct and iterative methods for linear systems, eigenvalue decompositions and QR/SVD factorizations, stability and accuracy of numerical algorithms, the IEEE floating point standard, sparse and structured matrices, preconditioning, linear algebra software. Problem sets require some knowledge of MATLAB®.

Subjects

numerical linear algebra | numerical linear algebra | linear systems | linear systems | eigenvalue decomposition | eigenvalue decomposition | QR/SVD factorization | QR/SVD factorization | numerical algorithms | numerical algorithms | IEEE floating point standard | IEEE floating point standard | sparse matrices | sparse matrices | structured matrices | structured matrices | preconditioning | preconditioning | linear algebra software | linear algebra software | Matlab | Matlab

License

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18.727 Topics in Algebraic Geometry: Algebraic Surfaces (MIT) 18.727 Topics in Algebraic Geometry: Algebraic Surfaces (MIT)

Description

The main aims of this seminar will be to go over the classification of surfaces (Enriques-Castelnuovo for characteristic zero, Bombieri-Mumford for characteristic p), while working out plenty of examples, and treating their geometry and arithmetic as far as possible. The main aims of this seminar will be to go over the classification of surfaces (Enriques-Castelnuovo for characteristic zero, Bombieri-Mumford for characteristic p), while working out plenty of examples, and treating their geometry and arithmetic as far as possible.

Subjects

near equivalence | near equivalence | algebraic equivalence | algebraic equivalence | numerical equivalence | numerical equivalence | birational | birational | rational | rational | maps | maps | surfaces | surfaces | ruled surfaces | ruled surfaces | rational surfaces | rational surfaces | linear systems | linear systems | castelnuovo's criterion | castelnuovo's criterion | rationality | rationality | picard | picard | albanese | albanese | classification | classification | K3 | K3 | elliptic | elliptic | Kodaira dimension | Kodaira dimension | bielliptic | bielliptic

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18.335J 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 problems

License

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2.086 Numerical Computation for Mechanical Engineers (MIT) 2.086 Numerical Computation for Mechanical Engineers (MIT)

Description

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

Subjects

MATLAB | MATLAB | numerical analysis | numerical analysis | programming | programming | physical modeling | physical modeling | calculus | calculus | linear algebra | linear algebra | Monte Carlo Method | Monte Carlo Method | differential equations | differential equations | nonlinear systems | nonlinear systems

License

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2.04A Systems and Controls (MIT) 2.04A Systems and Controls (MIT)

Description

This course provides an introduction to linear systems, transfer functions, and Laplace transforms. It covers stability and feedback, and provides basic design tools for specifications of transient response. It also briefly covers frequency-domain techniques.   This course provides an introduction to linear systems, transfer functions, and Laplace transforms. It covers stability and feedback, and provides basic design tools for specifications of transient response. It also briefly covers frequency-domain techniques.  

Subjects

systems | systems | controls | controls | ordinary differential equations | ordinary differential equations | ODEs | ODEs | differential equations | differential equations | Laplace | Laplace | transfer function | transfer function | flywheel | flywheel | circuits | circuits | impedance | impedance | feedback | feedback | root locus | root locus | linear systems | linear systems | Laplace transforms | Laplace transforms | stability | stability | frequency-domain | frequency-domain | skyscaper | skyscaper

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